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Glossary

OLR

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.[1] Detection and labeling of the different zones (or blocks) as text body, illustrations, math symbols, and tables embedded in a document is called geometric layout analysis.[2] But text zones play different logical roles inside the document (titles, captions, footnotes, etc.) and this kind of semantic labeling is the scope of the logical layout analysis.

Newspaper Layout Lexicon

Document layout analysis is the union of geometric and logical labeling. It is typically performed before a document image is sent to an OCR engine, but it can be used also to detect duplicate copies of the same document in large archives, or to index documents by their structure or pictorial content.

Document layout is formally defined in the international standard ISO 8613-1:1989.



You may also be interested in the following terms :

ALTO

The Analyzed Layout and Text Object (ALTO) is an open XML standard to represent information of OCR recognized texts.

IIIF

The International Image Interoperability Framework (IIIF) defines several application programming interfaces that provide a standardised method of describing and delivering images over the web, as well as “presentation based metadata” (that is, structural metadata) about structured sequences of images.

OCR

OCR (optical character recognition) is the recognition of printed or written text characters by a computer. This involves photoscanning of the text character-by-character, analysis of the scanned-in image, and then translation of the character image into character codes, such as ASCII, commonly used in data processing.

OLR

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.

Topic model

In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract “topics” that occur in a collection of documents. Topic modelling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body.