Creating word-level language models for large-vocabulary handwriting recognition
Identifieur interne : 007B68 ( Main/Exploration ); précédent : 007B67; suivant : 007B69Creating word-level language models for large-vocabulary handwriting recognition
Auteurs : John F. Pitrelli [États-Unis] ; Amit Roy [États-Unis]Source :
- International journal on document analysis and recognition : (Print) [ 1433-2833 ] ; 2003.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
We discuss development of a word-unigram language model for online handwriting recognition. First, we tokenize a text corpus into words, contrasting with tokenization methods designed for other purposes. Second, we select for our model a subset of the words found discussing deviations from an N-most-frequent-words approach. From a 600-million-word corpus, we generated a 53,000-word model which eliminates 45% of word-recognition errors made by a character-level-model baseline system. We anticipate that our methods will be applicable to offline recognition as well, and to some extent to other recognizers, such as speech recognizers and video retrieval systems.
Affiliations:
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Le document en format XML
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Pitrelli</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>IBM T.J. Watson Research Center, P.O. Box 218</s1><s2>Yorktown Heights, NY 10598</s2><s3>USA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>États-Unis</country><wicri:noRegion>Yorktown Heights, NY 10598</wicri:noRegion></affiliation></author><author><name sortKey="Roy, Amit" sort="Roy, Amit" uniqKey="Roy A" first="Amit" last="Roy">Amit Roy</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>IBM T.J. Watson Research Center, P.O. Box 218</s1><s2>Yorktown Heights, NY 10598</s2><s3>USA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>États-Unis</country><wicri:noRegion>Yorktown Heights, NY 10598</wicri:noRegion></affiliation></author></analytic><series><title level="j" type="main">International journal on document analysis and recognition : (Print)</title><title level="j" type="abbreviated">Int. j. doc. anal. recognit. : (Print)</title><idno type="ISSN">1433-2833</idno><imprint><date when="2003">2003</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">International journal on document analysis and recognition : (Print)</title><title level="j" type="abbreviated">Int. j. doc. anal. recognit. : (Print)</title><idno type="ISSN">1433-2833</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Character recognition</term><term>Handwriting recognition</term><term>Language recognition</term><term>Pattern recognition</term><term>Syntactic analysis</term><term>Token</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Reconnaissance écriture</term><term>Reconnaissance forme</term><term>Reconnaissance langage</term><term>Reconnaissance caractère</term><term>Analyse syntaxique</term><term>Unigram</term><term>Tokenization</term><term>Word-level language model</term><term>Jeton</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">We discuss development of a word-unigram language model for online handwriting recognition. First, we tokenize a text corpus into words, contrasting with tokenization methods designed for other purposes. Second, we select for our model a subset of the words found discussing deviations from an N-most-frequent-words approach. From a 600-million-word corpus, we generated a 53,000-word model which eliminates 45% of word-recognition errors made by a character-level-model baseline system. We anticipate that our methods will be applicable to offline recognition as well, and to some extent to other recognizers, such as speech recognizers and video retrieval systems.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country></list><tree><country name="États-Unis"><noRegion><name sortKey="Pitrelli, John F" sort="Pitrelli, John F" uniqKey="Pitrelli J" first="John F." last="Pitrelli">John F. Pitrelli</name></noRegion><name sortKey="Roy, Amit" sort="Roy, Amit" uniqKey="Roy A" first="Amit" last="Roy">Amit Roy</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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