OCR : Print -- An Overview
Identifieur interne : 000333 ( France/Analysis ); précédent : 000332; suivant : 000334OCR : Print -- An Overview
Auteurs : Abdel Belaïd [France]Source :
English descriptors
Abstract
Nowadays, there is much motivation to provide computerized document analysis systems. Giant steps have been made in the last decade, both in terms of technological supports and in software products. Character recognition ({\sc ocr}) contributes to this progress by providing techniques to convert large volumes of data automatically. There are so many papers and patents advertising recognition rates as high as 99.99 percent ; this gives the impression that automation problems seem to have been solved. However, the failure of some real applications show that performance problems subsist on composite and degraded documents (i.e. noisy characters, tilt, mixing of fonts, etc.) and that there is still room for progress. Various methods have been proposed to increase the accuracy of optical character recognizers. In fact, at various research laboratories, the challenge is to develop robust methods that remove as much as possible the typographical and noise restrictions while maintaining rates similar to those provided by limited-font commercial machines.
Affiliations:
- France
- Alsace-Champagne-Ardenne-Lorraine, Région Lorraine
- Nancy
- Centre national de la recherche scientifique, Institut national de recherche en informatique et en automatique, Laboratoire lorrain de recherche en informatique et ses applications, Université de Lorraine
Links toward previous steps (curation, corpus...)
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- to stream Main, to step Merge: 002A62
- to stream Main, to step Curation: 002909
- to stream Main, to step Exploration: 002909
- to stream France, to step Extraction: 000333
Links to Exploration step
CRIN:belaid95fLe document en format XML
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