Element gt:gt

Namespace http://www.ocr-d.de/GT/
Diagram
Element gt:gt / gt:state
Properties
content: complex
Model Element gt:gt / gt:state
Children Element gt:gt / gt:state
Instance
<gt:gt xmlns:gt="http://www.ocr-d.de/GT/">
  <gt:state prop="">{1,unbounded}</gt:state>
</gt:gt>
Schema location https://github.com/OCR-D/gt-labelling

Element gt:gt / gt:state

Namespace http://www.ocr-d.de/GT/
Diagram
Attribute gt:gt / gt:state / @prop
Properties
content: complex
minOccurs: 1
maxOccurs: unbounded
Attributes
QName Type Use
Attribute gt:gt / gt:state / @prop restriction of xsd:string optional
Usable attribute values
age
Age of data to process
age/historical
Of or concerning history or past events
age/historical/medieval
Relating to the Middle Ages.
age/contemporary
Belonging to or occurring in the present
age/ancient
Belonging to the very distant past and no longer in existence.
automation
Description coming soon.
automation/manual
Human interaction required Examples: Ground truthing Related: Performance evaluation
automation/automated
No interaction required Examples: OCR Related: Machine learning
automation/assisted
Some automation, but user interaction possible / required Examples: Auto-completion when typing Related: Trainable, Interactive
production-method
Production method of physical document (e.g. paper document such as a book)
production-method/manual
E.g. handwritten
production-method/machine
Description coming soon.
production-method/machine/printed
Description coming soon.
production-method/machine/printed/typeset
Printed using typesetting method
production-method/machine/printed/computer
Printed from computer or other electronic device using an office or similar printer
production-method/machine/typewritten
Description coming soon.
content-type
Description coming soon.
content-type/data
Description coming soon.
content-type/metadata
Description coming soon.
content-type/metadata/quality
Description coming soon.
content-type/metadata/quality/performance-info
Description coming soon.
content-type/metadata/features
Extracted features Examples: Word count of a text Related: Information extraction, Machine learning
content-type/metadata/structure
Structure of an object of some sort Examples: Document structure, Table structure
content-type/metadata/structure/toc
Table of contents of a book, newspaper etc.
content-type/metadata/annotations
Added data
content-type/metadata/authorship
Author attribution / information
content-type/metadata/spatial
Relating to space
content-type/metadata/spatial/location
Location or position
content-type/settings
E.g. tool configuration
content-type/model
A model for a concept. Examples: Page model to aid recognition
content-type/lexicon
A collection of data items organised / sorted in a certain way. Lexicon: the vocabulary of a person, language, or branch of knowledge
content-type/corpus
Corpus: a collection of written texts, especially the entire works of a particular author or a body of writing on a particular subject. Examples: A text corpus, An image database
precision
Description coming soon.
precision/ground-truth
Ground truth is a term used in various fields to refer to information provided by direct observation as opposed to information provided by inference. Gold standard: the best available under reasonable conditions
precision/measured
Measured (precise) Examples: OCR performance measured using ground truth
precision/estimated
Estimated by machine or human (not precise)
precision/random
Random data of some sort. Examples: a random number between 1 and 6 (dice)
precision/fuzzy
Statistical data are not always precise numbers, or vectors, or categories. Real data are frequently what is called fuzzy. Examples where this fuzziness is obvious are quality of life data, environmental, biological, medical, sociological and economics data. Also the results of measurements can be best described by using fuzzy numbers and fuzzy vectors respectively.
license
Software or data usage licence
license/free
Description coming soon.
license/free/non-commercial
Free for non-commercial use
license/paid-for
Description coming soon.
license/paid-for/pay-once
Description coming soon.
license/paid-for/volume
Description coming soon.
license/paid-for/subscription
Description coming soon.
license/openSource
Open-source software (OSS) is computer software with its source code made available with a license in which the copyright holder provides the rights to study, change, and distribute the software to anyone and for any purpose. Related: Free / paid for
platform
Supported platform
platform/windows
Description coming soon.
platform/macos
Description coming soon.
platform/linux
Description coming soon.
platform/platform-independent
Description coming soon.
platform/platform-independent/java
Description coming soon.
platform/platform-independent/web
Web service or web app
platform/mobile
Description coming soon.
platform/mobile/ios
Description coming soon.
platform/mobile/android
Description coming soon.
content-encoding
Description coming soon.
content-encoding/textual
Description coming soon.
content-encoding/textual/annotated
Textual content with annotations
content-encoding/textual/natural-language
Text represents natural language. Examples: A news article Related:
content-encoding/structured
E.g. XML
content-encoding/structured/tabular
Content encoded in tabular form Examples: A tab-separated table with headings and values
content-encoding/image
Description coming soon.
content-encoding/image/colour
Description coming soon.
content-encoding/image/bitonal
Description coming soon.
content-encoding/mathematical
Description coming soon.
content-encoding/mathematical/vector-based
E.g. polygonal
content-encoding/mathematical/vector-based/stroke-based
Examples: Online handwriting
content-encoding/mathematical/polygonal
Description coming soon.
activityDomain
General domain, research field or specific processing strategy of a workflow activity. Examples: An activity for automated number plate recognition could be labelled with "OCR" domain. Related: "Topic" of a data object
activityDomain/computing
Computing is any goal-oriented activity requiring, benefiting from, or creating a mathematical sequence of steps known as an algorithm — e.g. through computers. Examples: Any activity in document image analysis is from the domain of computing. Only steps such as physical document restoration should be excluded. Related: Data object "topic" such as Engineering
activityDomain/computing/visual
Visual computing is a generic term for all computer science disciplines handling with images and 3D models, i.e. computer graphics, image processing, visualization, computer vision, virtual and augmented reality, video processing, but also includes aspects of pattern recognition, human computer interaction, machine learning and digital libraries. Examples: See above Related: "Machine Learning" (separate label type)
activityDomain/computing/visual/imgVidProc
Image processing is processing of images using mathematical operations by using any form of signal processing for which the input is an image, a series of images, or a video, such as a photograph or video frame. Video processing is a particular case of signal processing, which often employs video filters and where the input and output signals are video files or video streams. Examples: Binarisation of a colour image Related: Content analysis (for information extraction) Computer graphics (for visualisation)
activityDomain/computing/visual/imgVidProc/geometric
Affine transsformation or other geometric operation applied to an image / video. An affine transformation is an important class of linear 2-D geometric transformations which maps variables (e.g. pixel intensity values located at position Eqn:eqnxy1 in an input image) into new variables (e.g. Eqn:eqnxy2 in an output image) by applying a linear combination of translation, rotation, scaling and/or shearing (i.e. non-uniform scaling in some directions) operations. Examples: Rotation, dewarping Related: Pixel-based operations
activityDomain/computing/visual/imgVidProc/pixel-based
Any image operation on pixel-level Examples: Binarisation, morphological operations Related: Geometric processing
activityDomain/computing/visual/analysisRecognition
Content analysis is "a wide and heterogeneous set of manual or computer-assisted techniques for contextualized interpretations of documents produced by communication processes in the strict sense of that phrase (any kind of text, written, iconic, multimedia, etc.) or signification processes (traces and artifacts), having as ultimate goal the production of valid and trustworthy inferences." Examples: Text recognition / OCR Related: Text processing (separate categoty) Performance evaluation (separate categoty)
activityDomain/computing/visual/analysisRecognition/text
Translation of any kind of depicted symbols to machine readable format Examples: OCR Mathematical equation recognition Related: Text processing (separate category) Table recognition Map reading
activityDomain/computing/visual/analysisRecognition/text/ocr
Optical character recognition (optical character reader, OCR) is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed on an image (for example from a television broadcast). Examples: Number plate reading Related: Mathematical equation recognition Map reading
activityDomain/computing/visual/analysisRecognition/text/maths
Specialised recognition of mathematical equations / formulas. Examples: Equations in scientific papers Related: OCR
activityDomain/computing/visual/analysisRecognition/text/date
Specialised recognition of dates and times Examples: Date printed on newspaper page Related: OCR
activityDomain/computing/visual/analysisRecognition/tables
The recognition of table/form structure and/or contents. Examples: Stock exchange data in a newspaper, Filled in questionnaire form Related: OCR Object / shape recognition (e.g. table separator detection)
activityDomain/computing/visual/analysisRecognition/charts
Recognition or analysis of data charts. Examples: Pie chart, Bar chart, Graphs Related: OCR, Object / shape recognition
activityDomain/computing/visual/analysisRecognition/maps
Recognition and analysis of maps or plans of any kind. Examples: Floor plans, Engineering drawings, Geographical maps Related: OCR, Object / shape recognition
activityDomain/computing/visual/analysisRecognition/shape
Object recognition is a process for identifying a specific object in a digital image or video. Object recognition algorithms rely on matching, learning, or pattern recognition algorithms using appearance-based or feature-based techniques. Common techniques include edges, gradients, Histogram of Oriented Gradients (HOG), Haar wavelets, and linear binary patterns. Examples: Logo recognition Fingerprint reading Related: Machine learning, Text and symbol recognition Forensic studies
activityDomain/computing/visual/analysisRecognition/shape/face
A facial recognition system is a computer application capable of identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database. Examples: Smartphone unlocking via detection of owner's face Related: Machine learning (separate category)
activityDomain/computing/visual/analysisRecognition/layoutAnalysis
In computer vision, document layout analysis is the process of identifying and categorizing the regions of interest in the scanned image of a text document. A reading system requires the segmentation of text zones from non-textual ones and the arrangement in their correct reading order. Examples: Page layout analysis (segmentation into regions, classification into text, graphic, table etc.) Related: "OCR": Often used as a synonym for layout analysis and text recognition, but strictly only the text recognition component.
activityDomain/computing/visual/graphics
Computer graphics are pictures and movies created using computers - usually referring to image data created by a computer specifically with help from specialized graphical hardware and software. Example: Text rendering Related: Presentation / visualisation (part of Data Creation / Transformation)
activityDomain/computing/text
In computing, the term text processing refers to the discipline of mechanizing the creation or manipulation of electronic text. Text usually refers to all the alphanumeric characters specified on the keyboard of the person performing the mechanization, but in general text here means the abstraction layer that is one layer above the standard character encoding of the target text. The term processing refers to automated (or mechanized) processing, as opposed to the same manipulation done manually. Text processing involves computer commands which invoke content, content changes, and cursor movement, for example to - search and replace - format - generate a processed report of the content of, or - filter a file or report of a text file. Related: Text recognition (Visual Computing)
activityDomain/computing/text/naturalLanguage
Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human–computer interaction. Many challenges in NLP involve: natural language understanding, enabling computers to derive meaning from human or natural language input; and others involve natural language generation. Examples: Digital assistants (e.g. in smartphones) Related: OCR
activityDomain/computing/text/naturalLanguage/identification
In natural language processing, language identification or language guessing is the problem of determining which natural language given content is in. Examples: Language identification to select a dictionary for OCR applications Related: OCR
activityDomain/computing/text/naturalLanguage/sentiment
Sentiment analysis (also known as opinion mining) refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials. Examples: A basic task in sentiment analysis is classifying the polarity of a given text at the document, sentence, or feature/aspect level — whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Related: Summarising
activityDomain/computing/text/naturalLanguage/summarising
Automatic summarization is the process of reducing a text document with a computer program in order to create a summary that retains the most important points of the original document. Technologies that can make a coherent summary take into account variables such as length, writing style and syntax. Examples: Automatic summary of a news article Related: Sentiment mining
activityDomain/computing/text/naturalLanguage/partOfSpeech
In corpus linguistics, part-of-speech tagging (POS tagging or POST), also called grammatical tagging or word-category disambiguation, is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context—i.e., its relationship with adjacent and related words in a phrase, sentence, or paragraph. Examples: A simplified form of this is commonly taught to school-age children, in the identification of words as nouns, verbs, adjectives, adverbs, etc. Related: Named entity recognition, Tokenisation (as part of Data creation / transformation)
activityDomain/computing/text/naturalLanguage/namedEntities
Named-entity recognition (NER) (also known as entity identification, entity chunking and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities in text into pre-defined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc. Related: Part-of-speech tagging Summarising
activityDomain/computing/machineLearning
Machine learning is a subfield of computer science[1] that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed". Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Examples: Decision tree learning, Artificial neural networks Related: Content analysis and recognition
activityDomain/computing/informationManagement
Information management (IM) concerns a cycle of organisational activity: the acquisition of information from one or more sources, the custodianship and the distribution of that information to those who need it, and its ultimate disposition through archiving or deletion. Data management comprises all the disciplines related to managing data as a valuable resource. Examples: Data access, Data security Document management system Related: Visualistation (as part of Data Creation / Transformation)
activityDomain/computing/informationManagement/retrieval
Data retrieval means obtaining data from a database management system such as ODBMS. In this case, it is considered that data is represented in a structured way, and there is no ambiguity in data. In order to retrieve the desired data the user present a set of criteria by a query. Examples: Retrieval of image from image database using pattern matching Related: Visualisation
activityDomain/computing/performanceEval
Measuring the performance of a given software system or method, returning for instance a quality value. Examples: OCR accuracy measurement Related: Information extraction Pattern matching
activityDomain/computing/performanceEval/comparative
Basic comparison of software systems or methods to decide which is better under given circumstances. Examples: Number of correctly recognised words of two OCR engines Related: Information extraction Ground truth
activityDomain/computing/performanceEval/in-depth
Performance analysis providing detail on the evaluation result in order to be able to understand the result and improve the methods / systems under investigation. Examples: Region-based layout analysis performance with merges, splits, misses, false detections etc., OCR accuracy with recognition statistics per character class Related: Information retrieval
activityDomain/computing/forensics
Forensic science is the application of science to criminal and civil laws. Forensic scientists collect, preserve, and analyze scientific evidence during the course of an investigation. Examples: Document verification / counterfeit detection Related: Face recognition
processingLevel
Distinction between low-level data processing (e.g. using a mathematical formula) and high-level processing that entails some form of recognition, reasoning or matching.
processingLevel/low-level
Data processing involving basic conversion, application of mathematical formulas or similar Examples: Image thresholding Image smoothing Text chunking (e.g. splitting into words) Related: Several visual computing approaches
processingLevel/high-level
Processing that entails some form of recognition, reasoning or matching, for example. Examples: OCR Face recognition Related: Natural language processing, Content analysis and recognition
processingLevel/high-level/detection
Methods involving some form of detection, identification, location or matching. Examples: Writer identification, Logo detection Related: Object recognition, OCR, Machine learning
processingLevel/high-level/detection/verification
Authentication (from Greek: αὐθεντικός authentikos, "real, genuine", from αὐθέντης authentes, "author") is the act of confirming the truth of an attribute of a single piece of data (a datum) claimed true by an entity. In contrast with identification which refers to the act of stating or otherwise indicating a claim purportedly attesting to a person or thing's identity, authentication is the process of actually confirming that identity. Examples: Signature verification Related: Forensic studies, Content analysis and recognition
processingLevel/high-level/classification
In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning. Examples: OCR Related: Machine learning, Content analysis and recognition
processingLevel/high-level/understanding
Highest level of processing including reasoning based on the actual meaning of the data that is beaing processed. Examples: Natural language understanding Related: Machine learning, Content analysis and recognition, Natural language processing
dataTransformation
Any action to creates or transforms data. Examples: Image acquisition, conversion, Text tokenisation, Annotation, Extraction
dataTransformation/acquisition
Data acquisition is the process of sampling signals that measure real world physical conditions and converting the resulting samples into digital numeric values that can be manipulated by a computer. Data acquisition systems, abbreviated by the acronyms DAS or DAQ, typically convert analog waveforms into digital values for processing. The components of data acquisition systems include: Sensors, to convert physical parameters to electrical signals. Signal conditioning circuitry, to convert sensor signals into a form that can be converted to digital values. Analog-to-digital converters, to convert conditioned sensor signals to digital values. Related: Conversion Retrieval
dataTransformation/conversion
Data conversion is the conversion of computer data from one format to another. Examples: JPG image to PNG image, UTF-8 encoded text to ASCII Related: Low-level processing
dataTransformation/segmentation
Splitting data into distinct parts or demarking the points where to split. Examples: Document page segmentation, Image segmentation, Foreground-background separation, Text tokeinsation / chunking Related: Content analysis / recognition Annotation / labelling
dataTransformation/enhancement
Removal of unwanted parts of data or adding/correcting data to improve readability, quality. Pre- or postprocessing of some kind. Examples: Noise removal in images, Geometric correction, Spelling correction, Watermark removal, Text restoration Related: Low-level processing
dataTransformation/enrichment
Adding data to increase information content Examples: Adding metadata Related: Part-of-speech tagging
dataTransformation/enrichment/annotation
Localised addition of information. Examples: Part-of-speech tagging, Named entity tagging, Page layout annotation (regions etc.) Related: Segmentation
dataTransformation/extraction
Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). Examples: Language and vocabulary analysis, Image understanding Related: High-level processing Content analysis and recognition
dataTransformation/visualisation
Information visualisation is the study of (interactive) visual representations of abstract data to reinforce human cognition. The abstract data include both numerical and non-numerical data, such as text and geographic information. Examples: Text rendering Chart creation Related: Conversion Computer graphics
adaptability
How well can the activity adapt to different circumstances. Examples: Trainable method, Interactive system
adaptability/configurable
A method that can be configured in some way to allow the explicit adaption to different use cases. Examples: OCR with settings for language, font etc. Related: Interactive Generic / unconstraint
adaptability/trainable
A method that can be trained by examples. Examples: OCR training to support a new type of font Related: Configurable, Interactive, Generic / unconstraint
adaptability/trainable/supervised
Supervised learning is the machine learning task of inferring a function from labeled training data.[1] The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. Examples: Labelled character images for training an OCR engine Related: Configurable Interactive
adaptability/trainable/unsupervised
Unsupervised learning is the machine learning task of inferring a function to describe hidden structure from unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution. This distinguishes unsupervised learning from supervised learning and reinforcement learning. Examples: Clustering Related: Machine learning
adaptability/interactive
A method that adapts according to user interaction. Examples: Dictionary expansion during spell checking Related: Configurable, Trainable
adaptability/generic
Method with wide applicability which therefore may not need to be trained or configured. Examples: Google multi-language OCR Related: Trainable, Configurable
maturity
System / method/ algorithm maturity. Examples: Prototype, Production system Related: Licence
maturity/stable
A stable release is available
maturity/experimental
Experimental, in development, prototype
maturity/industrial
Production-strength method / system that is reliable, tested, and robust
originalSource
Disregarding the current form of the data, where does it originate from, what was the original medium?
originalSource/produced
Data that has been composed, created, produced or rendered in some form. Examples: Book, Website Related: Content Encoding
originalSource/produced/physical
The data was orininally part of a physical medium Examples: Newspaper Whiteboard writing Related: Physical production method
originalSource/produced/physical/paper
The data was originally produced on paper Example: Printed magazine Related: Age
originalSource/produced/physical/paper/book
A paper book Examples: Notebook, Novel Related: Physical production method
originalSource/produced/physical/paper/newspaper
A printed newspaper Examples: The Guardian Related: Physical production method
originalSource/produced/physical/paper/magazine
A printed magazine. Usyually with more complex layout and formatting in comparison to books or newspapers. Examples: Time magazine Related: Physical production method
originalSource/produced/physical/paper/journal
A printed journal Examples: Science journal Related: Physical production method
originalSource/produced/physical/whiteboard
The data was originally produced on a whiteboard / flipchart / blackboard Examples: Whiteboard bullet points from a meeting Related: Physical production method
originalSource/produced/physical/poster
A poster or board of some kind Examples: A poster for a research paper Related: Physical production method
originalSource/produced/virtual
The data was created in / for the virtual space (digital) Examples: Word processor document Related: Content encoding
originalSource/produced/virtual/www
The data was created for the Internet. Examples: Wikipedia page Related: Data conversion, Visualisation
originalSource/captured
Data captured from the real world / the environment Examples: Photograph of a street Related: Acquisition
originalSource/captured/scenes
Scenes captured from the world Examples: A picture of a room with people Related: Acquisition
originalSource/captured/scenes/3D
Threedimensional scenes captured somehow
acquisition
Involved methods that lead from the source medium to the current state / format Examples: Scanning, Photocopying Related: Physical production method, Source medium
acquisition/analogToDigital
Conversion from any form of analog or physical data / medium to digital form. Examples: Digital photography, Scanning Related: Source medium
acquisition/analogToDigital/scanning
Capturing with digital scanner Examples: Flatbed scanner Related: Acquisition
acquisition/analogToDigital/camera
Camera-based digitisation Examples: Overhead scanner, Smartphone document capture Related: Acquisition method
acquisition/copied
Replicated in some way
acquisition/copied/photocopy
A document that was photocopied at some point
acquisition/copied/carbon-copy
The document is a carbon copy
acquisition/copied/microfilm
The document copied to microfilm or microfiche at some point
acquisition/copied/fax
The document was faxed (using a fax machine)
acquisition/synthesis
The combination of components or elements to form a connected whole Examples: Artificial ground truth (e.g. a synthetic newspaper page) Related: Acquisition Source medium
contentOfInterest
Source / target content. What is the interesting bit in the data at hand.
contentOfInterest/visual
Description coming soon.
contentOfInterest/visual/text
Description coming soon.
contentOfInterest/visual/graphical
Description coming soon.
contentOfInterest/visual/graphical/separator
Description coming soon.
contentOfInterest/visual/graphical/barcode
A barcode is a machine-readable representation of data relating to the object to which it is attached. Originally barcodes systematically represented data by varying the widths and spacings of parallel lines, and may be referred to as linear or one-dimensional (1D). Later two-dimensional (2D) codes were developed, using rectangles, dots, hexagons and other geometric patterns in two dimensions, usually called barcodes although they do not use bars as such. Barcodes originally were scanned by special optical scanners called barcode readers. Later applications software became available for devices that could read images, such as smartphones with cameras. Examples: - Barcode on a product - QR code representing a weblink
contentOfInterest/visual/image
Description coming soon.
contentOfInterest/visual/image/photograph
Description coming soon.
contentOfInterest/visual/image/photograph/person
Description coming soon.
contentOfInterest/visual/image/photograph/person/face
Description coming soon.
contentOfInterest/visual/image/drawing
Description coming soon.
contentOfInterest/visual/composite
Description coming soon.
contentOfInterest/visual/composite/tables
Description coming soon.
contentOfInterest/visual/composite/charts
Description coming soon.
contentOfInterest/visual/composite/maps
Description coming soon.
contentOfInterest/visual/composite/maths
Description coming soon.
contentOfInterest/visual/composite/chem
Description coming soon.
contentOfInterest/visual/composite/music
Description coming soon.
granularity
Description coming soon.
granularity/physical
E.g. segmentation
granularity/physical/document-related
Description coming soon.
granularity/physical/document-related/page
Description coming soon.
granularity/physical/document-related/region
Region, zone, block
granularity/physical/document-related/text-line
Description coming soon.
granularity/physical/document-related/word
Word or partial word, if separated by line break, for example
granularity/physical/document-related/glyph
In typography, a glyph is an elemental symbol within an agreed set of symbols, intended to represent a readable character for the purposes of writing
granularity/physical/document-related/double-page
Two facing pages (e.g. in book)
granularity/physical/natural-language
Description coming soon.
granularity/physical/natural-language/sentence
Description coming soon.
granularity/physical/natural-language/token
Description coming soon.
granularity/physical/natural-language/syllable
Description coming soon.
granularity/logical
Description coming soon.
granularity/logical/document-related
Description coming soon.
granularity/logical/document-related/document
A complete document Examples: Book
granularity/logical/document-related/chapter
Description coming soon.
granularity/logical/document-related/section
Description coming soon.
granularity/logical/document-related/article
Description coming soon.
granularity/logical/document-related/paragraph
Description coming soon.
granularity/logical/table
A table with columns and rows
granularity/logical/table/column
Table column
granularity/logical/table/row
Table row
granularity/logical/table/cell
Table cell
condition
Degradation, aging, damage etc.
condition/noise
Description coming soon.
condition/noise/speckles
Speckle-like noise
condition/noise/speckles/salt-and-pepper
Small, bright and dark dot-like noise
condition/noise/clutter
Larger noise 'objects'
condition/noise/clutter/thresholding-related
Image thresholding / binarisation-related noise
condition/production-related
Conditions introduced during the production of the medium / object
condition/production-related/document-characteristics
Document-related characteristics
condition/production-related/document-characteristics/pasted-clippings
Paper clippings pasted onto a page
condition/production-related/document-characteristics/textured-paper
Paper with a visible texture
condition/production-related/document-characteristics/uneven-character-spacing
Intra-word and inter-word character spacing is not uniform
condition/production-related/document-characteristics/narrow-border
The content of a page reaches very close to the page border or even touches it
condition/production-related/document-characteristics/low-contrast
The contrast between the paper and the page content is very low
condition/production-related/document-characteristics/halftoning
Dot-based halftoning printing technique was used (to emulate more colours / grey tones)
condition/production-related/document-characteristics/dithering
Dithering printing technique was used (added randomness to avoid unwanted patterns)
condition/production-related/document-faults
Fault introduced during document production
condition/production-related/document-faults/bleed-through
Ink bled through from back of page
condition/production-related/document-faults/ink-from-facing
Ink from facing page was transferred to this page
condition/production-related/document-faults/smeared-ink
Ink was smeared after printing / writing
condition/production-related/document-faults/touching-chars
Independent characters are touching due to printing issues
condition/production-related/document-faults/touching-chars/horizontally
Neighbouring characters within one text line are touching
condition/production-related/document-faults/touching-chars/vertically
Characters from neighbouring text lines are touching each other
condition/production-related/document-faults/uneven-ink-distrib
The ink was not distributed properly during printing, leading to unwanted empty or faint regions
condition/production-related/document-faults/filled-in-chars
Gaps or holes in characters are filled in (e.g. due to too much ink)
condition/production-related/document-faults/sort-shoulder-artefacts
Sort shoulder parts touched the paper during printing, leading to visible artefacts around characters
condition/production-related/document-faults/broken-chars
Some print characters were broken (bits missing) leading to repeated visible defects
condition/production-related/document-faults/faint-chars
Faint individual characters, e.g. when not using enough force during typewriting
condition/production-related/document-faults/blurred-chars
Blurred characters due to production issues
condition/production-related/document-faults/non-straight-text-lines
Text lines were not printed straight (printing issue / limitation)
condition/wear
Description coming soon.
condition/wear/medium-damage
The medium (e.g. paper) is damaged in some way
condition/wear/medium-damage/folds
E.g. paper folds
condition/wear/medium-damage/tears
Medium is torn
condition/wear/medium-damage/holes
Any kind of holes in the medium
condition/wear/medium-damage/holes/punch-holes
Punch holes visible
condition/wear/medium-damage/holes/unintended
Holes / missing parts in the medium due to damage
condition/wear/medium-damage/missing-parts
Whole parts of the medium are missing (e.g. torn off)
condition/wear/medium-damage/stains
Noticeable stains on medium
condition/wear/medium-damage/scratches
E.g. microfilm scratches
condition/wear/medium-damage/staples
Visible staples
condition/wear/additions
Things added to the medium during use
condition/wear/additions/repairs
The medium was visibly repaired
condition/wear/additions/repairs/paper-repairs
Paper was reapaired (e.g. with patches)
condition/wear/additions/repairs/clear-tape
Clear tape, visible through replections or darker colour
condition/wear/additions/informative
Additions containing information
condition/wear/additions/informative/annotations
Annotations regarding the content
condition/wear/additions/informative/stamps
The medium was stamped
condition/wear/additions/corrections
Content corrections
condition/wear/additions/corrections/manual
E.g. handwritten corrections of printed content
condition/ageing
Ageing or preservation-related issues
condition/ageing/warping
Arbitrary warping (e.g. due to moisture)
condition/ageing/discolouration
Discolouration of the medium of any kind
condition/ageing/discolouration/global
E.g. yellowish teint
condition/ageing/discolouration/edges
Local discolouration of the edges of the medium
condition/ageing/disintegraion
Disintegration of medium
condition/ageing/disintegraion/uneven-edges
Uneven medium edges due to disintegration
condition/ageing/mould
Visible damage through mould
condition/ageing/faded-content
Faded content (e.g. due to sunlight)
condition/acquisition
Description coming soon.
condition/acquisition/geometric
Geometric distortions etc.
condition/acquisition/geometric/skew
Skew / rotation
condition/acquisition/geometric/skew/global
The whole page is skewed
condition/acquisition/geometric/skew/non-uniform
Non-uniform skew, e.g. due to faulty scan feed
condition/acquisition/geometric/90-degree-rotation
Page rotated 90 degree left or right
condition/acquisition/geometric/upside-down
The object is represented upside down (e.g. scanned the wrong way around)
condition/acquisition/geometric/perspective-distortions
Perspective distortions (e.g. due to camera-based acquisition)
condition/acquisition/geometric/page-curl
Visible page curl (e.g. book scanning)
condition/acquisition/content-or-background
Content- or background-related issues
condition/acquisition/content-or-background/incomplete-capture
Not the whole content was captured during acquisition or copying
condition/acquisition/content-or-background/tight-margins
Tight / narrow margins
condition/acquisition/content-or-background/included-objects
Foreign objects visible
condition/acquisition/content-or-background/included-objects/preceeding-or-proceeding
Part of preceeding or succeeding object included (e.g. other page)
condition/acquisition/content-or-background/included-objects/medium-structure
Medium structure visible (e.g. book cover)
condition/acquisition/content-or-background/included-objects/clips
Paper clips visible
condition/acquisition/content-or-background/included-objects/fingers
Fingers visible
condition/acquisition/content-or-background/included-objects/insects
Insects visible
condition/acquisition/content-or-background/included-objects/background
Unwanted background visible (e.g. scanner bed)
condition/acquisition/method-flaws
Scanning or reproduction method flaws / issues
condition/acquisition/method-flaws/imaging
Imaging-related flaws
condition/acquisition/method-flaws/imaging/show-through
Parts of other page showing through (e.g. due to thin paper)
condition/acquisition/method-flaws/imaging/uneven-illumination
Uneven illumination leading to brightness or contrast variations
condition/acquisition/method-flaws/imaging/uneven-illumination/shadows
Shadows visible
condition/acquisition/method-flaws/imaging/out-of-focus
Object was not properly focused leading to blur
condition/acquisition/method-flaws/imaging/low-contrast
Low image contrast
condition/acquisition/method-flaws/imaging/missing-content
Some of the original content is missing or changed
condition/acquisition/method-flaws/imaging/missing-content/thresholding
Content or information loss due to thresholding / binaristaion
data-attributes
Description coming soon.
data-attributes/language
Language(s) of data
data-attributes/language/natural
E.g. a spoken language
data-attributes/language/natural/english
English language
data-attributes/language/mixed
More than one language used
data-attributes/document-related
Document attributes
data-attributes/document-related/visual
Any visual properties / attributes
data-attributes/document-related/visual/text
Text attributes
data-attributes/document-related/visual/text/script
Text script
data-attributes/document-related/visual/text/script/latin
Latin script
data-attributes/document-related/visual/text/script/braille
Braille script
data-attributes/document-related/visual/text/font
Font attributes
data-attributes/document-related/visual/text/font/cursive
Cursive font (italics or handwritten)
data-attributes/document-related/visual/text/font/monospace
Monospace font (all characters have the same width)
data-attributes/document-related/visual/text/font/typeface
General typeface or hand
data-attributes/document-related/visual/text/font/typeface/blackletter
Blackletter, gothic, Fraktur
data-attributes/document-related/visual/text/font/typeface/antiqua
Antiqua font (more modern)
data-attributes/document-related/visual/text/font/typeface/manuscript
Print-like manuscript font
data-attributes/document-related/visual/text/font/decorated
Text decorations to highlight or beautify
data-attributes/document-related/visual/text/font/decorated/flourishes
Flourishes added to the characters
data-attributes/document-related/visual/text/font/decorated/multi-colour
Multiple colours used for text (e.g. in one text line)
data-attributes/document-related/visual/text/font/decorated/reverse-video
Dark background, bright text colour
data-attributes/document-related/visual/text/font/multi-font
Multiple fonts used
data-attributes/document-related/visual/text/font/multi-font/typefaces
More than one typeface used
data-attributes/document-related/visual/text/font/multi-font/font-sizes
More than one font size used
data-attributes/document-related/visual/text/drop-caps
Drap capitals (large capitals at beginning of paragraph)
data-attributes/document-related/visual/columns
The content is arranged in columns or one column
data-attributes/document-related/visual/columns/one
One-column text
data-attributes/document-related/visual/columns/two
Two-column text
data-attributes/document-related/visual/columns/multiple
Multi-column text (more than two)
data-attributes/document-related/visual/rotated-content
Some content is rotated with respect to other content
data-attributes/document-related/visual/complex-background
Background not just plain white / colour
data-attributes/document-related/visual/complex-background/watermarks
Watermark(s) in background
data-attributes/document-related/visual/complex-background/impressions
Impressions / embossings visible
data-attributes/document-related/visual/illustrations
Illustrations in content
data-attributes/document-related/visual/illustrations/multi-colour
Multi-colour illustrations in content
data-attributes/document-related/visual/decorations
Decorations of some kind
data-attributes/document-related/visual/decorations/frames
Some content enclosed in frames or borders
data-attributes/document-related/visual/line-art
Line drawings / line art
data-attributes/document-related/visual/captchas
CAPTCHAs to verify a human user
data-attributes/document-related/structural
Document structure-related
data-attributes/document-related/structural/running-titles
Titles repeated each page
data-attributes/document-related/structural/footnotes
Footnotes at bottom of page
data-attributes/document-related/structural/references
Bibliographic references on page
topic
Description coming soon.
topic/economy
Description coming soon.
topic/economy/financial
Description coming soon.
topic/economy/financial/checks
Description coming soon.
topic/economy/financial/invoices
Description coming soon.
topic/economy/financial/bank-notes
Description coming soon.
topic/social-science
Description coming soon.
topic/social-science/maps
Description coming soon.
topic/social-science/maps/topographical
Description coming soon.
topic/social-science/maps/road
Description coming soon.
topic/social-science/maps/land-use
Description coming soon.
topic/social-science/traffic
Description coming soon.
topic/social-science/traffic/number-plates
Description coming soon.
topic/social-science/traffic/signs
Description coming soon.
topic/engineering
Description coming soon.
topic/engineering/architecture
Description coming soon.
topic/engineering/architecture/floor-plans
Description coming soon.
topic/engineering/architecture/drawings
Description coming soon.
topic/engineering/medical
Description coming soon.
topic/engineering/engineering-drawings
Description coming soon.
topic/engineering/patents
Description coming soon.
topic/media
Description coming soon.
topic/media/adverts
Description coming soon.
topic/computing
Description coming soon.
user-groups
Description coming soon.
user-groups/admins
Description coming soon.
user-groups/workflow-experts
Description coming soon.
user-groups/domain-experts
Description coming soon.
user-groups/domain-experts/dia
Description coming soon.
user-groups/domain-experts/librarians
Description coming soon.
Schema location https://github.com/OCR-D/gt-labelling

Attribute gt:gt / gt:state / @prop

Namespace No namespace
Annotations
Usable attribute values
age
Age of data to process
age/historical
Of or concerning history or past events
age/historical/medieval
Relating to the Middle Ages.
age/contemporary
Belonging to or occurring in the present
age/ancient
Belonging to the very distant past and no longer in existence.
automation
Description coming soon.
automation/manual
Human interaction required Examples: Ground truthing Related: Performance evaluation
automation/automated
No interaction required Examples: OCR Related: Machine learning
automation/assisted
Some automation, but user interaction possible / required Examples: Auto-completion when typing Related: Trainable, Interactive
production-method
Production method of physical document (e.g. paper document such as a book)
production-method/manual
E.g. handwritten
production-method/machine
Description coming soon.
production-method/machine/printed
Description coming soon.
production-method/machine/printed/typeset
Printed using typesetting method
production-method/machine/printed/computer
Printed from computer or other electronic device using an office or similar printer
production-method/machine/typewritten
Description coming soon.
content-type
Description coming soon.
content-type/data
Description coming soon.
content-type/metadata
Description coming soon.
content-type/metadata/quality
Description coming soon.
content-type/metadata/quality/performance-info
Description coming soon.
content-type/metadata/features
Extracted features Examples: Word count of a text Related: Information extraction, Machine learning
content-type/metadata/structure
Structure of an object of some sort Examples: Document structure, Table structure
content-type/metadata/structure/toc
Table of contents of a book, newspaper etc.
content-type/metadata/annotations
Added data
content-type/metadata/authorship
Author attribution / information
content-type/metadata/spatial
Relating to space
content-type/metadata/spatial/location
Location or position
content-type/settings
E.g. tool configuration
content-type/model
A model for a concept. Examples: Page model to aid recognition
content-type/lexicon
A collection of data items organised / sorted in a certain way. Lexicon: the vocabulary of a person, language, or branch of knowledge
content-type/corpus
Corpus: a collection of written texts, especially the entire works of a particular author or a body of writing on a particular subject. Examples: A text corpus, An image database
precision
Description coming soon.
precision/ground-truth
Ground truth is a term used in various fields to refer to information provided by direct observation as opposed to information provided by inference. Gold standard: the best available under reasonable conditions
precision/measured
Measured (precise) Examples: OCR performance measured using ground truth
precision/estimated
Estimated by machine or human (not precise)
precision/random
Random data of some sort. Examples: a random number between 1 and 6 (dice)
precision/fuzzy
Statistical data are not always precise numbers, or vectors, or categories. Real data are frequently what is called fuzzy. Examples where this fuzziness is obvious are quality of life data, environmental, biological, medical, sociological and economics data. Also the results of measurements can be best described by using fuzzy numbers and fuzzy vectors respectively.
license
Software or data usage licence
license/free
Description coming soon.
license/free/non-commercial
Free for non-commercial use
license/paid-for
Description coming soon.
license/paid-for/pay-once
Description coming soon.
license/paid-for/volume
Description coming soon.
license/paid-for/subscription
Description coming soon.
license/openSource
Open-source software (OSS) is computer software with its source code made available with a license in which the copyright holder provides the rights to study, change, and distribute the software to anyone and for any purpose. Related: Free / paid for
platform
Supported platform
platform/windows
Description coming soon.
platform/macos
Description coming soon.
platform/linux
Description coming soon.
platform/platform-independent
Description coming soon.
platform/platform-independent/java
Description coming soon.
platform/platform-independent/web
Web service or web app
platform/mobile
Description coming soon.
platform/mobile/ios
Description coming soon.
platform/mobile/android
Description coming soon.
content-encoding
Description coming soon.
content-encoding/textual
Description coming soon.
content-encoding/textual/annotated
Textual content with annotations
content-encoding/textual/natural-language
Text represents natural language. Examples: A news article Related:
content-encoding/structured
E.g. XML
content-encoding/structured/tabular
Content encoded in tabular form Examples: A tab-separated table with headings and values
content-encoding/image
Description coming soon.
content-encoding/image/colour
Description coming soon.
content-encoding/image/bitonal
Description coming soon.
content-encoding/mathematical
Description coming soon.
content-encoding/mathematical/vector-based
E.g. polygonal
content-encoding/mathematical/vector-based/stroke-based
Examples: Online handwriting
content-encoding/mathematical/polygonal
Description coming soon.
activityDomain
General domain, research field or specific processing strategy of a workflow activity. Examples: An activity for automated number plate recognition could be labelled with "OCR" domain. Related: "Topic" of a data object
activityDomain/computing
Computing is any goal-oriented activity requiring, benefiting from, or creating a mathematical sequence of steps known as an algorithm — e.g. through computers. Examples: Any activity in document image analysis is from the domain of computing. Only steps such as physical document restoration should be excluded. Related: Data object "topic" such as Engineering
activityDomain/computing/visual
Visual computing is a generic term for all computer science disciplines handling with images and 3D models, i.e. computer graphics, image processing, visualization, computer vision, virtual and augmented reality, video processing, but also includes aspects of pattern recognition, human computer interaction, machine learning and digital libraries. Examples: See above Related: "Machine Learning" (separate label type)
activityDomain/computing/visual/imgVidProc
Image processing is processing of images using mathematical operations by using any form of signal processing for which the input is an image, a series of images, or a video, such as a photograph or video frame. Video processing is a particular case of signal processing, which often employs video filters and where the input and output signals are video files or video streams. Examples: Binarisation of a colour image Related: Content analysis (for information extraction) Computer graphics (for visualisation)
activityDomain/computing/visual/imgVidProc/geometric
Affine transsformation or other geometric operation applied to an image / video. An affine transformation is an important class of linear 2-D geometric transformations which maps variables (e.g. pixel intensity values located at position Eqn:eqnxy1 in an input image) into new variables (e.g. Eqn:eqnxy2 in an output image) by applying a linear combination of translation, rotation, scaling and/or shearing (i.e. non-uniform scaling in some directions) operations. Examples: Rotation, dewarping Related: Pixel-based operations
activityDomain/computing/visual/imgVidProc/pixel-based
Any image operation on pixel-level Examples: Binarisation, morphological operations Related: Geometric processing
activityDomain/computing/visual/analysisRecognition
Content analysis is "a wide and heterogeneous set of manual or computer-assisted techniques for contextualized interpretations of documents produced by communication processes in the strict sense of that phrase (any kind of text, written, iconic, multimedia, etc.) or signification processes (traces and artifacts), having as ultimate goal the production of valid and trustworthy inferences." Examples: Text recognition / OCR Related: Text processing (separate categoty) Performance evaluation (separate categoty)
activityDomain/computing/visual/analysisRecognition/text
Translation of any kind of depicted symbols to machine readable format Examples: OCR Mathematical equation recognition Related: Text processing (separate category) Table recognition Map reading
activityDomain/computing/visual/analysisRecognition/text/ocr
Optical character recognition (optical character reader, OCR) is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed on an image (for example from a television broadcast). Examples: Number plate reading Related: Mathematical equation recognition Map reading
activityDomain/computing/visual/analysisRecognition/text/maths
Specialised recognition of mathematical equations / formulas. Examples: Equations in scientific papers Related: OCR
activityDomain/computing/visual/analysisRecognition/text/date
Specialised recognition of dates and times Examples: Date printed on newspaper page Related: OCR
activityDomain/computing/visual/analysisRecognition/tables
The recognition of table/form structure and/or contents. Examples: Stock exchange data in a newspaper, Filled in questionnaire form Related: OCR Object / shape recognition (e.g. table separator detection)
activityDomain/computing/visual/analysisRecognition/charts
Recognition or analysis of data charts. Examples: Pie chart, Bar chart, Graphs Related: OCR, Object / shape recognition
activityDomain/computing/visual/analysisRecognition/maps
Recognition and analysis of maps or plans of any kind. Examples: Floor plans, Engineering drawings, Geographical maps Related: OCR, Object / shape recognition
activityDomain/computing/visual/analysisRecognition/shape
Object recognition is a process for identifying a specific object in a digital image or video. Object recognition algorithms rely on matching, learning, or pattern recognition algorithms using appearance-based or feature-based techniques. Common techniques include edges, gradients, Histogram of Oriented Gradients (HOG), Haar wavelets, and linear binary patterns. Examples: Logo recognition Fingerprint reading Related: Machine learning, Text and symbol recognition Forensic studies
activityDomain/computing/visual/analysisRecognition/shape/face
A facial recognition system is a computer application capable of identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database. Examples: Smartphone unlocking via detection of owner's face Related: Machine learning (separate category)
activityDomain/computing/visual/analysisRecognition/layoutAnalysis
In computer vision, document layout analysis is the process of identifying and categorizing the regions of interest in the scanned image of a text document. A reading system requires the segmentation of text zones from non-textual ones and the arrangement in their correct reading order. Examples: Page layout analysis (segmentation into regions, classification into text, graphic, table etc.) Related: "OCR": Often used as a synonym for layout analysis and text recognition, but strictly only the text recognition component.
activityDomain/computing/visual/graphics
Computer graphics are pictures and movies created using computers - usually referring to image data created by a computer specifically with help from specialized graphical hardware and software. Example: Text rendering Related: Presentation / visualisation (part of Data Creation / Transformation)
activityDomain/computing/text
In computing, the term text processing refers to the discipline of mechanizing the creation or manipulation of electronic text. Text usually refers to all the alphanumeric characters specified on the keyboard of the person performing the mechanization, but in general text here means the abstraction layer that is one layer above the standard character encoding of the target text. The term processing refers to automated (or mechanized) processing, as opposed to the same manipulation done manually. Text processing involves computer commands which invoke content, content changes, and cursor movement, for example to - search and replace - format - generate a processed report of the content of, or - filter a file or report of a text file. Related: Text recognition (Visual Computing)
activityDomain/computing/text/naturalLanguage
Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human–computer interaction. Many challenges in NLP involve: natural language understanding, enabling computers to derive meaning from human or natural language input; and others involve natural language generation. Examples: Digital assistants (e.g. in smartphones) Related: OCR
activityDomain/computing/text/naturalLanguage/identification
In natural language processing, language identification or language guessing is the problem of determining which natural language given content is in. Examples: Language identification to select a dictionary for OCR applications Related: OCR
activityDomain/computing/text/naturalLanguage/sentiment
Sentiment analysis (also known as opinion mining) refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials. Examples: A basic task in sentiment analysis is classifying the polarity of a given text at the document, sentence, or feature/aspect level — whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral. Related: Summarising
activityDomain/computing/text/naturalLanguage/summarising
Automatic summarization is the process of reducing a text document with a computer program in order to create a summary that retains the most important points of the original document. Technologies that can make a coherent summary take into account variables such as length, writing style and syntax. Examples: Automatic summary of a news article Related: Sentiment mining
activityDomain/computing/text/naturalLanguage/partOfSpeech
In corpus linguistics, part-of-speech tagging (POS tagging or POST), also called grammatical tagging or word-category disambiguation, is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context—i.e., its relationship with adjacent and related words in a phrase, sentence, or paragraph. Examples: A simplified form of this is commonly taught to school-age children, in the identification of words as nouns, verbs, adjectives, adverbs, etc. Related: Named entity recognition, Tokenisation (as part of Data creation / transformation)
activityDomain/computing/text/naturalLanguage/namedEntities
Named-entity recognition (NER) (also known as entity identification, entity chunking and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities in text into pre-defined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc. Related: Part-of-speech tagging Summarising
activityDomain/computing/machineLearning
Machine learning is a subfield of computer science[1] that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed". Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Examples: Decision tree learning, Artificial neural networks Related: Content analysis and recognition
activityDomain/computing/informationManagement
Information management (IM) concerns a cycle of organisational activity: the acquisition of information from one or more sources, the custodianship and the distribution of that information to those who need it, and its ultimate disposition through archiving or deletion. Data management comprises all the disciplines related to managing data as a valuable resource. Examples: Data access, Data security Document management system Related: Visualistation (as part of Data Creation / Transformation)
activityDomain/computing/informationManagement/retrieval
Data retrieval means obtaining data from a database management system such as ODBMS. In this case, it is considered that data is represented in a structured way, and there is no ambiguity in data. In order to retrieve the desired data the user present a set of criteria by a query. Examples: Retrieval of image from image database using pattern matching Related: Visualisation
activityDomain/computing/performanceEval
Measuring the performance of a given software system or method, returning for instance a quality value. Examples: OCR accuracy measurement Related: Information extraction Pattern matching
activityDomain/computing/performanceEval/comparative
Basic comparison of software systems or methods to decide which is better under given circumstances. Examples: Number of correctly recognised words of two OCR engines Related: Information extraction Ground truth
activityDomain/computing/performanceEval/in-depth
Performance analysis providing detail on the evaluation result in order to be able to understand the result and improve the methods / systems under investigation. Examples: Region-based layout analysis performance with merges, splits, misses, false detections etc., OCR accuracy with recognition statistics per character class Related: Information retrieval
activityDomain/computing/forensics
Forensic science is the application of science to criminal and civil laws. Forensic scientists collect, preserve, and analyze scientific evidence during the course of an investigation. Examples: Document verification / counterfeit detection Related: Face recognition
processingLevel
Distinction between low-level data processing (e.g. using a mathematical formula) and high-level processing that entails some form of recognition, reasoning or matching.
processingLevel/low-level
Data processing involving basic conversion, application of mathematical formulas or similar Examples: Image thresholding Image smoothing Text chunking (e.g. splitting into words) Related: Several visual computing approaches
processingLevel/high-level
Processing that entails some form of recognition, reasoning or matching, for example. Examples: OCR Face recognition Related: Natural language processing, Content analysis and recognition
processingLevel/high-level/detection
Methods involving some form of detection, identification, location or matching. Examples: Writer identification, Logo detection Related: Object recognition, OCR, Machine learning
processingLevel/high-level/detection/verification
Authentication (from Greek: αὐθεντικός authentikos, "real, genuine", from αὐθέντης authentes, "author") is the act of confirming the truth of an attribute of a single piece of data (a datum) claimed true by an entity. In contrast with identification which refers to the act of stating or otherwise indicating a claim purportedly attesting to a person or thing's identity, authentication is the process of actually confirming that identity. Examples: Signature verification Related: Forensic studies, Content analysis and recognition
processingLevel/high-level/classification
In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning. Examples: OCR Related: Machine learning, Content analysis and recognition
processingLevel/high-level/understanding
Highest level of processing including reasoning based on the actual meaning of the data that is beaing processed. Examples: Natural language understanding Related: Machine learning, Content analysis and recognition, Natural language processing
dataTransformation
Any action to creates or transforms data. Examples: Image acquisition, conversion, Text tokenisation, Annotation, Extraction
dataTransformation/acquisition
Data acquisition is the process of sampling signals that measure real world physical conditions and converting the resulting samples into digital numeric values that can be manipulated by a computer. Data acquisition systems, abbreviated by the acronyms DAS or DAQ, typically convert analog waveforms into digital values for processing. The components of data acquisition systems include: Sensors, to convert physical parameters to electrical signals. Signal conditioning circuitry, to convert sensor signals into a form that can be converted to digital values. Analog-to-digital converters, to convert conditioned sensor signals to digital values. Related: Conversion Retrieval
dataTransformation/conversion
Data conversion is the conversion of computer data from one format to another. Examples: JPG image to PNG image, UTF-8 encoded text to ASCII Related: Low-level processing
dataTransformation/segmentation
Splitting data into distinct parts or demarking the points where to split. Examples: Document page segmentation, Image segmentation, Foreground-background separation, Text tokeinsation / chunking Related: Content analysis / recognition Annotation / labelling
dataTransformation/enhancement
Removal of unwanted parts of data or adding/correcting data to improve readability, quality. Pre- or postprocessing of some kind. Examples: Noise removal in images, Geometric correction, Spelling correction, Watermark removal, Text restoration Related: Low-level processing
dataTransformation/enrichment
Adding data to increase information content Examples: Adding metadata Related: Part-of-speech tagging
dataTransformation/enrichment/annotation
Localised addition of information. Examples: Part-of-speech tagging, Named entity tagging, Page layout annotation (regions etc.) Related: Segmentation
dataTransformation/extraction
Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). Examples: Language and vocabulary analysis, Image understanding Related: High-level processing Content analysis and recognition
dataTransformation/visualisation
Information visualisation is the study of (interactive) visual representations of abstract data to reinforce human cognition. The abstract data include both numerical and non-numerical data, such as text and geographic information. Examples: Text rendering Chart creation Related: Conversion Computer graphics
adaptability
How well can the activity adapt to different circumstances. Examples: Trainable method, Interactive system
adaptability/configurable
A method that can be configured in some way to allow the explicit adaption to different use cases. Examples: OCR with settings for language, font etc. Related: Interactive Generic / unconstraint
adaptability/trainable
A method that can be trained by examples. Examples: OCR training to support a new type of font Related: Configurable, Interactive, Generic / unconstraint
adaptability/trainable/supervised
Supervised learning is the machine learning task of inferring a function from labeled training data.[1] The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. Examples: Labelled character images for training an OCR engine Related: Configurable Interactive
adaptability/trainable/unsupervised
Unsupervised learning is the machine learning task of inferring a function to describe hidden structure from unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution. This distinguishes unsupervised learning from supervised learning and reinforcement learning. Examples: Clustering Related: Machine learning
adaptability/interactive
A method that adapts according to user interaction. Examples: Dictionary expansion during spell checking Related: Configurable, Trainable
adaptability/generic
Method with wide applicability which therefore may not need to be trained or configured. Examples: Google multi-language OCR Related: Trainable, Configurable
maturity
System / method/ algorithm maturity. Examples: Prototype, Production system Related: Licence
maturity/stable
A stable release is available
maturity/experimental
Experimental, in development, prototype
maturity/industrial
Production-strength method / system that is reliable, tested, and robust
originalSource
Disregarding the current form of the data, where does it originate from, what was the original medium?
originalSource/produced
Data that has been composed, created, produced or rendered in some form. Examples: Book, Website Related: Content Encoding
originalSource/produced/physical
The data was orininally part of a physical medium Examples: Newspaper Whiteboard writing Related: Physical production method
originalSource/produced/physical/paper
The data was originally produced on paper Example: Printed magazine Related: Age
originalSource/produced/physical/paper/book
A paper book Examples: Notebook, Novel Related: Physical production method
originalSource/produced/physical/paper/newspaper
A printed newspaper Examples: The Guardian Related: Physical production method
originalSource/produced/physical/paper/magazine
A printed magazine. Usyually with more complex layout and formatting in comparison to books or newspapers. Examples: Time magazine Related: Physical production method
originalSource/produced/physical/paper/journal
A printed journal Examples: Science journal Related: Physical production method
originalSource/produced/physical/whiteboard
The data was originally produced on a whiteboard / flipchart / blackboard Examples: Whiteboard bullet points from a meeting Related: Physical production method
originalSource/produced/physical/poster
A poster or board of some kind Examples: A poster for a research paper Related: Physical production method
originalSource/produced/virtual
The data was created in / for the virtual space (digital) Examples: Word processor document Related: Content encoding
originalSource/produced/virtual/www
The data was created for the Internet. Examples: Wikipedia page Related: Data conversion, Visualisation
originalSource/captured
Data captured from the real world / the environment Examples: Photograph of a street Related: Acquisition
originalSource/captured/scenes
Scenes captured from the world Examples: A picture of a room with people Related: Acquisition
originalSource/captured/scenes/3D
Threedimensional scenes captured somehow
acquisition
Involved methods that lead from the source medium to the current state / format Examples: Scanning, Photocopying Related: Physical production method, Source medium
acquisition/analogToDigital
Conversion from any form of analog or physical data / medium to digital form. Examples: Digital photography, Scanning Related: Source medium
acquisition/analogToDigital/scanning
Capturing with digital scanner Examples: Flatbed scanner Related: Acquisition
acquisition/analogToDigital/camera
Camera-based digitisation Examples: Overhead scanner, Smartphone document capture Related: Acquisition method
acquisition/copied
Replicated in some way
acquisition/copied/photocopy
A document that was photocopied at some point
acquisition/copied/carbon-copy
The document is a carbon copy
acquisition/copied/microfilm
The document copied to microfilm or microfiche at some point
acquisition/copied/fax
The document was faxed (using a fax machine)
acquisition/synthesis
The combination of components or elements to form a connected whole Examples: Artificial ground truth (e.g. a synthetic newspaper page) Related: Acquisition Source medium
contentOfInterest
Source / target content. What is the interesting bit in the data at hand.
contentOfInterest/visual
Description coming soon.
contentOfInterest/visual/text
Description coming soon.
contentOfInterest/visual/graphical
Description coming soon.
contentOfInterest/visual/graphical/separator
Description coming soon.
contentOfInterest/visual/graphical/barcode
A barcode is a machine-readable representation of data relating to the object to which it is attached. Originally barcodes systematically represented data by varying the widths and spacings of parallel lines, and may be referred to as linear or one-dimensional (1D). Later two-dimensional (2D) codes were developed, using rectangles, dots, hexagons and other geometric patterns in two dimensions, usually called barcodes although they do not use bars as such. Barcodes originally were scanned by special optical scanners called barcode readers. Later applications software became available for devices that could read images, such as smartphones with cameras. Examples: - Barcode on a product - QR code representing a weblink
contentOfInterest/visual/image
Description coming soon.
contentOfInterest/visual/image/photograph
Description coming soon.
contentOfInterest/visual/image/photograph/person
Description coming soon.
contentOfInterest/visual/image/photograph/person/face
Description coming soon.
contentOfInterest/visual/image/drawing
Description coming soon.
contentOfInterest/visual/composite
Description coming soon.
contentOfInterest/visual/composite/tables
Description coming soon.
contentOfInterest/visual/composite/charts
Description coming soon.
contentOfInterest/visual/composite/maps
Description coming soon.
contentOfInterest/visual/composite/maths
Description coming soon.
contentOfInterest/visual/composite/chem
Description coming soon.
contentOfInterest/visual/composite/music
Description coming soon.
granularity
Description coming soon.
granularity/physical
E.g. segmentation
granularity/physical/document-related
Description coming soon.
granularity/physical/document-related/page
Description coming soon.
granularity/physical/document-related/region
Region, zone, block
granularity/physical/document-related/text-line
Description coming soon.
granularity/physical/document-related/word
Word or partial word, if separated by line break, for example
granularity/physical/document-related/glyph
In typography, a glyph is an elemental symbol within an agreed set of symbols, intended to represent a readable character for the purposes of writing
granularity/physical/document-related/double-page
Two facing pages (e.g. in book)
granularity/physical/natural-language
Description coming soon.
granularity/physical/natural-language/sentence
Description coming soon.
granularity/physical/natural-language/token
Description coming soon.
granularity/physical/natural-language/syllable
Description coming soon.
granularity/logical
Description coming soon.
granularity/logical/document-related
Description coming soon.
granularity/logical/document-related/document
A complete document Examples: Book
granularity/logical/document-related/chapter
Description coming soon.
granularity/logical/document-related/section
Description coming soon.
granularity/logical/document-related/article
Description coming soon.
granularity/logical/document-related/paragraph
Description coming soon.
granularity/logical/table
A table with columns and rows
granularity/logical/table/column
Table column
granularity/logical/table/row
Table row
granularity/logical/table/cell
Table cell
condition
Degradation, aging, damage etc.
condition/noise
Description coming soon.
condition/noise/speckles
Speckle-like noise
condition/noise/speckles/salt-and-pepper
Small, bright and dark dot-like noise
condition/noise/clutter
Larger noise 'objects'
condition/noise/clutter/thresholding-related
Image thresholding / binarisation-related noise
condition/production-related
Conditions introduced during the production of the medium / object
condition/production-related/document-characteristics
Document-related characteristics
condition/production-related/document-characteristics/pasted-clippings
Paper clippings pasted onto a page
condition/production-related/document-characteristics/textured-paper
Paper with a visible texture
condition/production-related/document-characteristics/uneven-character-spacing
Intra-word and inter-word character spacing is not uniform
condition/production-related/document-characteristics/narrow-border
The content of a page reaches very close to the page border or even touches it
condition/production-related/document-characteristics/low-contrast
The contrast between the paper and the page content is very low
condition/production-related/document-characteristics/halftoning
Dot-based halftoning printing technique was used (to emulate more colours / grey tones)
condition/production-related/document-characteristics/dithering
Dithering printing technique was used (added randomness to avoid unwanted patterns)
condition/production-related/document-faults
Fault introduced during document production
condition/production-related/document-faults/bleed-through
Ink bled through from back of page
condition/production-related/document-faults/ink-from-facing
Ink from facing page was transferred to this page
condition/production-related/document-faults/smeared-ink
Ink was smeared after printing / writing
condition/production-related/document-faults/touching-chars
Independent characters are touching due to printing issues
condition/production-related/document-faults/touching-chars/horizontally
Neighbouring characters within one text line are touching
condition/production-related/document-faults/touching-chars/vertically
Characters from neighbouring text lines are touching each other
condition/production-related/document-faults/uneven-ink-distrib
The ink was not distributed properly during printing, leading to unwanted empty or faint regions
condition/production-related/document-faults/filled-in-chars
Gaps or holes in characters are filled in (e.g. due to too much ink)
condition/production-related/document-faults/sort-shoulder-artefacts
Sort shoulder parts touched the paper during printing, leading to visible artefacts around characters
condition/production-related/document-faults/broken-chars
Some print characters were broken (bits missing) leading to repeated visible defects
condition/production-related/document-faults/faint-chars
Faint individual characters, e.g. when not using enough force during typewriting
condition/production-related/document-faults/blurred-chars
Blurred characters due to production issues
condition/production-related/document-faults/non-straight-text-lines
Text lines were not printed straight (printing issue / limitation)
condition/wear
Description coming soon.
condition/wear/medium-damage
The medium (e.g. paper) is damaged in some way
condition/wear/medium-damage/folds
E.g. paper folds
condition/wear/medium-damage/tears
Medium is torn
condition/wear/medium-damage/holes
Any kind of holes in the medium
condition/wear/medium-damage/holes/punch-holes
Punch holes visible
condition/wear/medium-damage/holes/unintended
Holes / missing parts in the medium due to damage
condition/wear/medium-damage/missing-parts
Whole parts of the medium are missing (e.g. torn off)
condition/wear/medium-damage/stains
Noticeable stains on medium
condition/wear/medium-damage/scratches
E.g. microfilm scratches
condition/wear/medium-damage/staples
Visible staples
condition/wear/additions
Things added to the medium during use
condition/wear/additions/repairs
The medium was visibly repaired
condition/wear/additions/repairs/paper-repairs
Paper was reapaired (e.g. with patches)
condition/wear/additions/repairs/clear-tape
Clear tape, visible through replections or darker colour
condition/wear/additions/informative
Additions containing information
condition/wear/additions/informative/annotations
Annotations regarding the content
condition/wear/additions/informative/stamps
The medium was stamped
condition/wear/additions/corrections
Content corrections
condition/wear/additions/corrections/manual
E.g. handwritten corrections of printed content
condition/ageing
Ageing or preservation-related issues
condition/ageing/warping
Arbitrary warping (e.g. due to moisture)
condition/ageing/discolouration
Discolouration of the medium of any kind
condition/ageing/discolouration/global
E.g. yellowish teint
condition/ageing/discolouration/edges
Local discolouration of the edges of the medium
condition/ageing/disintegraion
Disintegration of medium
condition/ageing/disintegraion/uneven-edges
Uneven medium edges due to disintegration
condition/ageing/mould
Visible damage through mould
condition/ageing/faded-content
Faded content (e.g. due to sunlight)
condition/acquisition
Description coming soon.
condition/acquisition/geometric
Geometric distortions etc.
condition/acquisition/geometric/skew
Skew / rotation
condition/acquisition/geometric/skew/global
The whole page is skewed
condition/acquisition/geometric/skew/non-uniform
Non-uniform skew, e.g. due to faulty scan feed
condition/acquisition/geometric/90-degree-rotation
Page rotated 90 degree left or right
condition/acquisition/geometric/upside-down
The object is represented upside down (e.g. scanned the wrong way around)
condition/acquisition/geometric/perspective-distortions
Perspective distortions (e.g. due to camera-based acquisition)
condition/acquisition/geometric/page-curl
Visible page curl (e.g. book scanning)
condition/acquisition/content-or-background
Content- or background-related issues
condition/acquisition/content-or-background/incomplete-capture
Not the whole content was captured during acquisition or copying
condition/acquisition/content-or-background/tight-margins
Tight / narrow margins
condition/acquisition/content-or-background/included-objects
Foreign objects visible
condition/acquisition/content-or-background/included-objects/preceeding-or-proceeding
Part of preceeding or succeeding object included (e.g. other page)
condition/acquisition/content-or-background/included-objects/medium-structure
Medium structure visible (e.g. book cover)
condition/acquisition/content-or-background/included-objects/clips
Paper clips visible
condition/acquisition/content-or-background/included-objects/fingers
Fingers visible
condition/acquisition/content-or-background/included-objects/insects
Insects visible
condition/acquisition/content-or-background/included-objects/background
Unwanted background visible (e.g. scanner bed)
condition/acquisition/method-flaws
Scanning or reproduction method flaws / issues
condition/acquisition/method-flaws/imaging
Imaging-related flaws
condition/acquisition/method-flaws/imaging/show-through
Parts of other page showing through (e.g. due to thin paper)
condition/acquisition/method-flaws/imaging/uneven-illumination
Uneven illumination leading to brightness or contrast variations
condition/acquisition/method-flaws/imaging/uneven-illumination/shadows
Shadows visible
condition/acquisition/method-flaws/imaging/out-of-focus
Object was not properly focused leading to blur
condition/acquisition/method-flaws/imaging/low-contrast
Low image contrast
condition/acquisition/method-flaws/imaging/missing-content
Some of the original content is missing or changed
condition/acquisition/method-flaws/imaging/missing-content/thresholding
Content or information loss due to thresholding / binaristaion
data-attributes
Description coming soon.
data-attributes/language
Language(s) of data
data-attributes/language/natural
E.g. a spoken language
data-attributes/language/natural/english
English language
data-attributes/language/mixed
More than one language used
data-attributes/document-related
Document attributes
data-attributes/document-related/visual
Any visual properties / attributes
data-attributes/document-related/visual/text
Text attributes
data-attributes/document-related/visual/text/script
Text script
data-attributes/document-related/visual/text/script/latin
Latin script
data-attributes/document-related/visual/text/script/braille
Braille script
data-attributes/document-related/visual/text/font
Font attributes
data-attributes/document-related/visual/text/font/cursive
Cursive font (italics or handwritten)
data-attributes/document-related/visual/text/font/monospace
Monospace font (all characters have the same width)
data-attributes/document-related/visual/text/font/typeface
General typeface or hand
data-attributes/document-related/visual/text/font/typeface/blackletter
Blackletter, gothic, Fraktur
data-attributes/document-related/visual/text/font/typeface/antiqua
Antiqua font (more modern)
data-attributes/document-related/visual/text/font/typeface/manuscript
Print-like manuscript font
data-attributes/document-related/visual/text/font/decorated
Text decorations to highlight or beautify
data-attributes/document-related/visual/text/font/decorated/flourishes
Flourishes added to the characters
data-attributes/document-related/visual/text/font/decorated/multi-colour
Multiple colours used for text (e.g. in one text line)
data-attributes/document-related/visual/text/font/decorated/reverse-video
Dark background, bright text colour
data-attributes/document-related/visual/text/font/multi-font
Multiple fonts used
data-attributes/document-related/visual/text/font/multi-font/typefaces
More than one typeface used
data-attributes/document-related/visual/text/font/multi-font/font-sizes
More than one font size used
data-attributes/document-related/visual/text/drop-caps
Drap capitals (large capitals at beginning of paragraph)
data-attributes/document-related/visual/columns
The content is arranged in columns or one column
data-attributes/document-related/visual/columns/one
One-column text
data-attributes/document-related/visual/columns/two
Two-column text
data-attributes/document-related/visual/columns/multiple
Multi-column text (more than two)
data-attributes/document-related/visual/rotated-content
Some content is rotated with respect to other content
data-attributes/document-related/visual/complex-background
Background not just plain white / colour
data-attributes/document-related/visual/complex-background/watermarks
Watermark(s) in background
data-attributes/document-related/visual/complex-background/impressions
Impressions / embossings visible
data-attributes/document-related/visual/illustrations
Illustrations in content
data-attributes/document-related/visual/illustrations/multi-colour
Multi-colour illustrations in content
data-attributes/document-related/visual/decorations
Decorations of some kind
data-attributes/document-related/visual/decorations/frames
Some content enclosed in frames or borders
data-attributes/document-related/visual/line-art
Line drawings / line art
data-attributes/document-related/visual/captchas
CAPTCHAs to verify a human user
data-attributes/document-related/structural
Document structure-related
data-attributes/document-related/structural/running-titles
Titles repeated each page
data-attributes/document-related/structural/footnotes
Footnotes at bottom of page
data-attributes/document-related/structural/references
Bibliographic references on page
topic
Description coming soon.
topic/economy
Description coming soon.
topic/economy/financial
Description coming soon.
topic/economy/financial/checks
Description coming soon.
topic/economy/financial/invoices
Description coming soon.
topic/economy/financial/bank-notes
Description coming soon.
topic/social-science
Description coming soon.
topic/social-science/maps
Description coming soon.
topic/social-science/maps/topographical
Description coming soon.
topic/social-science/maps/road
Description coming soon.
topic/social-science/maps/land-use
Description coming soon.
topic/social-science/traffic
Description coming soon.
topic/social-science/traffic/number-plates
Description coming soon.
topic/social-science/traffic/signs
Description coming soon.
topic/engineering
Description coming soon.
topic/engineering/architecture
Description coming soon.
topic/engineering/architecture/floor-plans
Description coming soon.
topic/engineering/architecture/drawings
Description coming soon.
topic/engineering/medical
Description coming soon.
topic/engineering/engineering-drawings
Description coming soon.
topic/engineering/patents
Description coming soon.
topic/media
Description coming soon.
topic/media/adverts
Description coming soon.
topic/computing
Description coming soon.
user-groups
Description coming soon.
user-groups/admins
Description coming soon.
user-groups/workflow-experts
Description coming soon.
user-groups/domain-experts
Description coming soon.
user-groups/domain-experts/dia
Description coming soon.
user-groups/domain-experts/librarians
Description coming soon.
Type restriction of xsd:string
Properties
content: simple
Used by
Schema location https://github.com/OCR-D/gt-labelling