<?xml version="1.0" encoding="UTF-8" standalone="no"?>

<Ontology version="3">

<LabelTypeHierarchies>

<LabelType caption="Age" name="age">

<Description>Age of data to process</Description>

<LabelType caption="Historical" name="historical">

<Description>Of or concerning history or past events</Description>

<LabelType caption="Medieval" name="medieval">

<Description>Relating to the Middle Ages.</Description>

</LabelType>

</LabelType>

<LabelType caption="Contemporary" name="contemporary">

<Description>Belonging to or occurring in the present</Description>

</LabelType>

<LabelType caption="Ancient" name="ancient">

<Description>Belonging to the very distant past and no longer in existence.</Description>

</LabelType>

</LabelType>

<LabelType caption="Automation" name="automation">

<LabelType caption="Manual" name="manual">

<Description>Human interaction required

Examples:

Ground truthing

Related:

Performance evaluation</Description>

</LabelType>

<LabelType caption="Automated" name="automated">

<Description>No interaction required

Examples:

OCR

Related:

Machine learning</Description>

</LabelType>

<LabelType caption="Machine assisted / user-driven" name="assisted">

<Description>Some automation, but user interaction possible / required

Examples:

Auto-completion when typing

Related:

Trainable,

Interactive</Description>

</LabelType>

</LabelType>

<LabelType caption="Physical Production Method" name="production-method">

<Description>Production method of physical document (e.g. paper document such as a book)</Description>

<LabelType caption="Manual" name="manual">

<Description>E.g. handwritten</Description>

</LabelType>

<LabelType caption="Machine" name="machine">

<LabelType caption="Printed" name="printed">

<LabelType caption="Typeset" name="typeset">

<Description>Printed using typesetting method</Description>

</LabelType>

<LabelType caption="Computer printout" name="computer">

<Description>Printed from computer or other electronic device using an office or similar printer</Description>

</LabelType>

</LabelType>

<LabelType caption="Typewritten" name="typewritten"/>

</LabelType>

</LabelType>

<LabelType caption="Content Type" name="content-type">

<LabelType caption="Data" name="data"/>

<LabelType caption="Metadata" name="metadata">

<LabelType caption="Quality" name="quality">

<LabelType caption="Performance Information" name="performance-info"/>

</LabelType>

<LabelType caption="Features" name="features">

<Description>Extracted features

Examples:

Word count of a text

Related:

Information extraction,

Machine learning</Description>

</LabelType>

<LabelType caption="Structure" name="structure">

<Description>Structure of an object of some sort

Examples:

Document structure,

Table structure</Description>

<LabelType caption="Table of contents" name="toc">

<Description>Table of contents of a book, newspaper etc.</Description>

</LabelType>

</LabelType>

<LabelType caption="Annotations" name="annotations">

<Description>Added data</Description>

</LabelType>

<LabelType caption="Authorship" name="authorship">

<Description>Author attribution / information</Description>

</LabelType>

<LabelType caption="Spatial" name="spatial">

<Description>Relating to space</Description>

<LabelType caption="Location" name="location">

<Description>Location or position</Description>

</LabelType>

</LabelType>

</LabelType>

<LabelType caption="Settings" name="settings">

<Description>E.g. tool configuration</Description>

</LabelType>

<LabelType caption="Model" name="model">

<Description>A model for a concept.

Examples:

Page model to aid recognition</Description>

</LabelType>

<LabelType caption="Lexicon / index" name="lexicon">

<Description>A collection of data items organised / sorted in a certain way.

Lexicon: the vocabulary of a person, language, or branch of knowledge</Description>

</LabelType>

<LabelType caption="Corpus / database" name="corpus">

<Description>

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</Description>

</LabelType>

</LabelType>

<LabelType caption="Precision" name="precision">

<LabelType caption="Ground Truth / gold standard" name="ground-truth">

<Description>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</Description>

</LabelType>

<LabelType caption="Measured" name="measured">

<Description>Measured (precise)

Examples:

OCR performance measured using ground truth</Description>

</LabelType>

<LabelType caption="Estimated" name="estimated">

<Description>Estimated by machine or human (not precise)</Description>

</LabelType>

<LabelType caption="Random" name="random">

<Description>Random data of some sort.

Examples:

a random number between 1 and 6 (dice)</Description>

</LabelType>

<LabelType caption="Fuzzy" name="fuzzy">

<Description>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.</Description>

</LabelType>

</LabelType>

<LabelType caption="Licence" name="license">

<Description>Software or data usage licence</Description>

<LabelType caption="Free" name="free">

<LabelType caption="Non-commercial" name="non-commercial">

<Description>Free for non-commercial use</Description>

</LabelType>

</LabelType>

<LabelType caption="Paid for" name="paid-for">

<LabelType caption="Pay once" name="pay-once"/>

<LabelType caption="Volume" name="volume"/>

<LabelType caption="Subscription" name="subscription"/>

</LabelType>

<LabelType caption="Open Source" name="openSource">

<Description>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</Description>

</LabelType>

</LabelType>

<LabelType caption="Platform" name="platform">

<Description>Supported platform</Description>

<LabelType caption="Windows" name="windows"/>

<LabelType caption="Mac OS" name="macos"/>

<LabelType caption="Linux" name="linux"/>

<LabelType caption="Platform independent" name="platform-independent">

<LabelType caption="Java" name="java"/>

<LabelType caption="Web" name="web">

<Description>Web service or web app</Description>

</LabelType>

</LabelType>

<LabelType caption="Mobile" name="mobile">

<LabelType caption="iOS" name="ios"/>

<LabelType caption="Android" name="android"/>

</LabelType>

</LabelType>

<LabelType caption="Content Encoding" name="content-encoding">

<LabelType caption="Textual" name="textual">

<LabelType caption="Annotated" name="annotated">

<Description>Textual content with annotations</Description>

</LabelType>

<LabelType caption="Natural language" name="natural-language">

<Description>Text represents natural language.

Examples:

A news artcile

Related:

</Description>

</LabelType>

</LabelType>

<LabelType caption="Structured" name="structured">

<Description>E.g. XML</Description>

<LabelType caption="Tabular" name="tabular">

<Description>Content encoded in tabular form

Examples:

A tab-separated table with headings and values</Description>

</LabelType>

</LabelType>

<LabelType caption="Raster image" name="image">

<LabelType caption="Colour Image" name="colour"/>

<LabelType caption="Bitonal" name="bitonal"/>

</LabelType>

<LabelType caption="Mathematical / geometrical" name="mathematical">

<LabelType caption="Vector-based" name="vector-based">

<Description>E.g. polygonal</Description>

<LabelType caption="Stroke-based" name="stroke-based">

<Description>

Examples:

Online handwriting</Description>

</LabelType>

</LabelType>

<LabelType caption="Polygonal" name="polygonal"/>

</LabelType>

</LabelType>

<LabelType caption="Activity Domain" name="activityDomain">

<Description>General domain, research field or specific processing strategy of a workflow activty.

Examples:

An activity for automated number plate recognition could be labelled with "OCR" domain.

Related:

"Topic" of a data object</Description>

<LabelType caption="Computing" name="computing">

<Description>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</Description>

<LabelType caption="Visual Computing" name="visual">

<Description>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)</Description>

<LabelType caption="Image and video processing" name="imgVidProc">

<Description>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)</Description>

<LabelType caption="Geometric image/video processing" name="geometric">

<Description>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</Description>

</LabelType>

<LabelType caption="Pixel-based image/video processing" name="pixel-based">

<Description>Any image operation on pixel-level

Examples:

Binarisation, morphological operations

Related:

Geometric processing</Description>

</LabelType>

</LabelType>

<LabelType caption="Content analysis and recognition" name="analysisRecognition">

<Description>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)</Description>

<LabelType caption="Text and symbol recognition" name="text">

<Description>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</Description>

<LabelType caption="OCR" name="ocr">

<Description>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</Description>

</LabelType>

<LabelType caption="Mathematical expression recognition" name="maths">

<Description>Specialised recognition of mathematical equations / formulas.

Examples:

Equations in scientific papers

Related:

OCR</Description>

</LabelType>

<LabelType caption="Date recognition" name="date">

<Description>Specialised recognition of dates and times

Examples:

Date printed on newspaper page

Related:

OCR</Description>

</LabelType>

</LabelType>

<LabelType caption="Table / form analysis and recognition" name="tables">

<Description>The recognition of table/form structure and/or contents.

Examples:

Stock exchange data in a newspaper,

Filled in questionaire form

Related:

OCR

Object / shape recognition (e.g. table separator detection)</Description>

</LabelType>

<LabelType caption="Chart recognition" name="charts">

<Description>Recognition or analysis of data charts.

Examples:

Pie chart,

Bar chart,

Graphs

Related:

OCR,

Object / shape recognition</Description>

</LabelType>

<LabelType caption="Map and plan reading" name="maps">

<Description>Recognition and analysis of maps or plans of any kind.

Examples:

Floor plans,

Engineering drawings,

Geographical maps

Related:

OCR,

Object / shape recognition</Description>

</LabelType>

<LabelType caption="Object / shape recognition" name="shape">

<Description>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</Description>

<LabelType caption="Face recognition" name="face">

<Description>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)</Description>

</LabelType>

</LabelType>

<LabelType caption="Layout analysis" name="layoutAnalysis">

<Description>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.</Description>

</LabelType>

</LabelType>

<LabelType caption="Computer graphics" name="graphics">

<Description>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)</Description>

</LabelType>

</LabelType>

<LabelType caption="Text processing" name="text">

<Description>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)</Description>

<LabelType caption="Natural language processing" name="naturalLanguage">

<Description>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:

Digitial assistents (e.g. in smartphones)

Related:

OCR</Description>

<LabelType caption="Language identification" name="identification">

<Description>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</Description>

</LabelType>

<LabelType caption="Sentiment mining" name="sentiment">

<Description>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</Description>

</LabelType>

<LabelType caption="Summarising" name="summarising">

<Description>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</Description>

</LabelType>

<LabelType caption="Part-of-speech tagging" name="partOfSpeech">

<Description>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 entitiy recognition,

Tokenisation (as part of Data creation / transformation)</Description>

</LabelType>

<LabelType caption="Named entity recognition" name="namedEntities">

<Description>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</Description>

</LabelType>

</LabelType>

</LabelType>

<LabelType caption="Machine learning" name="machineLearning">

<Description>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

</Description>

</LabelType>

<LabelType caption="Information Management" name="informationManagement">

<Description>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)</Description>

<LabelType caption="Data retrieval" name="retrieval">

<Description>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</Description>

</LabelType>

</LabelType>

<LabelType caption="Performance evaluation" name="performanceEval">

<Description>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</Description>

<LabelType caption="Comparative performance analysis" name="comparative">

<Description>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</Description>

</LabelType>

<LabelType caption="In-depth performance analysis" name="in-depth">

<Description>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</Description>

</LabelType>

</LabelType>

<LabelType caption="Forensic studies" name="forensics">

<Description>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</Description>

</LabelType>

</LabelType>

</LabelType>

<LabelType caption="Processing Level" name="processingLevel">

<Description>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.</Description>

<LabelType caption="Low-level processing" name="low-level">

<Description>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</Description>

</LabelType>

<LabelType caption="High-level processing" name="high-level">

<Description>Processing that entails some form of recognition, reasoning or matching, for example.

Examples:

OCR

Face recognition

Related:

Natural language processing,

Content analysis and recognition</Description>

<LabelType caption="Detection / Identification" name="detection">

<Description>Methods involving some form of detection, identification, location or matching.

Examples:

Writer identification,

Logo detection

Related:

Object recognition,

OCR,

Machine learning</Description>

<LabelType caption="Verification / authentication" name="verification">

<Description>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</Description>

</LabelType>

</LabelType>

<LabelType caption="Classification / recognition" name="classification">

<Description>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</Description>

</LabelType>

<LabelType caption="Understanding" name="understanding">

<Description>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</Description>

</LabelType>

</LabelType>

</LabelType>

<LabelType caption="Data Creation / Transformation" name="dataTransformation">

<Description>Any action to creates or transforms data.

Examples:

Image acquisition,

conversion,

Text tokenisation,

Annotation,

Extraction</Description>

<LabelType caption="Acquisition" name="acquisition">

<Description>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</Description>

</LabelType>

<LabelType caption="Conversion" name="conversion">

<Description>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</Description>

</LabelType>

<LabelType caption="Segmentation / tokenisation" name="segmentation">

<Description>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</Description>

</LabelType>

<LabelType caption="Enhancement" name="enhancement">

<Description>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

</Description>

</LabelType>

<LabelType caption="Enrichment" name="enrichment">

<Description>Adding data to increase information content

Examples:

Adding metadata

Related:

Part-of-speech tagging</Description>

<LabelType caption="Annotation / labelling" name="annotation">

<Description>Localised addition of information.

Examples:

Part-of-speech tagging,

Named entitiy tagging,

Page layout annotation (regions etc.)

Related:

Segmentation</Description>

</LabelType>

</LabelType>

<LabelType caption="Information extraction" name="extraction">

<Description>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</Description>

</LabelType>

<LabelType caption="Visualisation / presentation" name="visualisation">

<Description>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</Description>

</LabelType>

</LabelType>

<LabelType caption="Adaptability / Applicability" name="adaptability">

<Description>How well can the activity adapt to different circumstances.

Examples:

Trainable method,

Interactive system</Description>

<LabelType caption="Configurable" name="configurable">

<Description>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</Description>

</LabelType>

<LabelType caption="Trainable" name="trainable">

<Description>A method that can be trained by examples.

Examples:

OCR training to support a new type of font

Related:

Configurable,

Interactive,

Generic / unconstraint</Description>

<LabelType caption="Supervised" name="supervised">

<Description>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</Description>

</LabelType>

<LabelType caption="Unsupervised" name="unsupervised">

<Description>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</Description>

</LabelType>

</LabelType>

<LabelType caption="Interactive" name="interactive">

<Description>A method that adapts according to user interaction.

Examples:

Dictionary expansion during spell checking

Related:

Configurable,

Trainable</Description>

</LabelType>

<LabelType caption="Generic / unconstraint" name="generic">

<Description>Method with wide applicability which therefore may not need to be trained or configured.

Examples:

Google multi-language OCR

Related:

Trainable,

Configurable</Description>

</LabelType>

</LabelType>

<LabelType caption="Maturity" name="maturity">

<Description>System / method/ algorithm maturity.

Examples:

Prototype,

Production system

Related:

Licence</Description>

<LabelType caption="Stable" name="stable">

<Description>A stable release is available</Description>

</LabelType>

<LabelType caption="Experimental" name="experimental">

<Description>Experimental, in development, prototype</Description>

</LabelType>

<LabelType caption="Industrial" name="industrial">

<Description>Production-strengh method / system that is reliable, tested, and robust</Description>

</LabelType>

</LabelType>

<LabelType caption="Original Source" name="originalSource">

<Description>Disregarding the current form of the data, where does it originate from, what was the original medium?</Description>

<LabelType caption="Produced data" name="produced">

<Description>Data that has been composed, created, produced or rendered in some form.

Examples:

Book,

Website

Related:

Content Encoding</Description>

<LabelType caption="Physical source medium" name="physical">

<Description>The data was orininally part of a physical medium

Examples:

Newspaper

Whiteboard writing

Related:

Physical production method</Description>

<LabelType caption="Paper document" name="paper">

<Description>The data was orignially produced on paper

Example:

Printed magazine

Related:

Age</Description>

<LabelType caption="Book" name="book">

<Description>A paper book

Examples:

Notebook,

Novel

Related:

Physical production method</Description>

</LabelType>

<LabelType caption="Newspaper" name="newspaper">

<Description>A printed newspaper

Examples:

The Guardian

Related:

Physical production method</Description>

</LabelType>

<LabelType caption="Magazine" name="magazine">

<Description>A printed magazine.

Usyually with more complex layout and formatting in comparison to books or newspapers.

Examples:

Time magazine

Related:

Physical production method</Description>

</LabelType>

<LabelType caption="Journal" name="journal">

<Description>A printed journal

Examples:

Science journal

Related:

Physical production method</Description>

</LabelType>

</LabelType>

<LabelType caption="Whiteboard / blackboard" name="whiteboard">

<Description>The data was originally produced on a whiteboard / flipchart / blackboard

Examples:

Whiteboard bullet points from a meeting

Related:

Physical production method</Description>

</LabelType>

<LabelType caption="Poster" name="poster">

<Description>A poster or board of some kind

Examples:

A poster for a research paper

Related:

Physical production method</Description>

</LabelType>

</LabelType>

<LabelType caption="Virtual source medium" name="virtual">

<Description>The data was created in / for the virtual space (digital)

Examples:

Word processor document

Related:

Content encoding</Description>

<LabelType caption="World Wide Web" name="www">

<Description>The data was created for the Internet.

Examples:

Wikipedia page

Related:

Data conversion,

Visualisation</Description>

</LabelType>

</LabelType>

</LabelType>

<LabelType caption="Captured data" name="captured">

<Description>Data captured from the real world / the environment

Examples:

Photograph of a street

Related:

Acquisition</Description>

<LabelType caption="Real / natural scenes" name="scenes">

<Description>Scenes captured from the world

Examples:

A picture of a room with people

Related:

Acquisition</Description>

<LabelType caption="3D scenes" name="3D">

<Description>Threedimensional scenes captured somehow</Description>

</LabelType>

</LabelType>

</LabelType>

</LabelType>

<LabelType caption="Acquisition / Replication Method" name="acquisition">

<Description>Involved methods that lead from the source medium to the current state / format

Examples:

Scanning,

Photocopying

Related:

Physical production method,

Source medium</Description>

<LabelType caption="Analog / physical to digital" name="analogToDigital">

<Description>Conversion from any form of analog or physical data / medium to digital form.

Examples:

Digital photography,

Scanning

Related:

Source medium</Description>

<LabelType caption="Scanning" name="scanning">

<Description>Capturing with digital scanner

Examples:

Flatbed scanner

Related:

Acquisition</Description>

</LabelType>

<LabelType caption="Camera" name="camera">

<Description>Camera-based digitisation

Examples:

Overhead scanner,

Smartphone document capture

Related:

Acquisition method</Description>

</LabelType>

</LabelType>

<LabelType caption="Copied" name="copied">

<Description>Replicated in some way</Description>

<LabelType caption="Photocopy" name="photocopy">

<Description>A document that was photocopied at some point</Description>

</LabelType>

<LabelType caption="Carbon copy" name="carbon-copy">

<Description>The document is a carbon copy</Description>

</LabelType>

<LabelType caption="Microfilm / microfiche" name="microfilm">

<Description>The document copied to microfilm or microfiche at some point</Description>

</LabelType>

<LabelType caption="Faxed" name="fax">

<Description>The document was faxed (using a fax machine)</Description>

</LabelType>

</LabelType>

<LabelType caption="Synthesis" name="synthesis">

<Description>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</Description>

</LabelType>

</LabelType>

<LabelType caption="Content of Interest" name="contentOfInterest">

<Description>Source / target content. What is the intersting bit in the data at hand.</Description>

<LabelType caption="Visual content" name="visual">

<LabelType caption="Text" name="text"/>

<LabelType caption="Graphical" name="graphical">

<LabelType caption="Separators" name="separator"/>