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24 septembre 2015
- handle: 10670/1.9a9tfa
- hal: hal-01444484
- doi: 10.1007/978-3-319-24369-6_34
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info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-24369-6_34
http://creativecommons.org/licenses/by/ , info:eu-repo/semantics/OpenAccess
Mots-clés
OCR Feature extraction Shape descriptorsSujets proches
Instruction and study--Methods Natural science Sciences Science of science Competence Design perception Pattern recognition Instruction and study--MethodsCiter ce document
Dawid Sas et al., « Comprehensive Performance Evaluation of Various Feature Extraction Methods for OCR Purposes », HAL-SHS : sciences de l'information, de la communication et des bibliothèques, ID : 10.1007/978-3-319-24369-6_34
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Résumé
Optical Character Recognition (OCR) is a very extensive branch of pattern recognition. The existence of super effective software designed for omnifont text recognition, capable of handling multiple languages, creates an impression that all problems in this field have already been solved. Indeed, focus of research in the OCR domain has constantly been shifting from offline, typewritten, Latin character recognition towards Asiatic alphabets, handwritten scripts and online process. Still, however, it is difficult to come across an elaboration which would not only cover the topic of numerous feature extraction methods for printed, Latin derived, isolated characters conceptually, but which would also attempt to implement, compare and optimize them in an experimental way. This paper aims at closing this gap by thoroughly examining the performance of several statistical methods with respect to their recognition rate and time efficiency.