Cross-learning in analytic word recognition without segmentation
Identifieur interne : 001376 ( Istex/Curation ); précédent : 001375; suivant : 001377Cross-learning in analytic word recognition without segmentation
Auteurs : C. Choisy ; A. BelaïdSource :
- International Journal on Document Analysis and Recognition [ 1433-2833 ] ; 2002-07-01.
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Abstract
Abstract.: In this paper a method for analytic handwritten word recognition based on causal Markov random fields is described. The word models are hmms where each state corresponds to a letter modeled by a nshp-hmm (Markov field). The word models are built dynamically. Training is operated using Baum-Welch algorithm where the parameters are reestimated on the generated word models. The segmentation is unnecessary: the system determines itself during training the best repartition of the information within the letter models. First experiments on two real databases of French check amount words give very encouraging results up to 86% for recognition without rejection.
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DOI: 10.1007/s100320200078
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C. Choisy<affiliation><mods:affiliation>LORIA/CNRS, Campus scientifique, BP 239, 54506 Vandoeuvre-les-Nancy, France; e-mail: {choisy,abelaid}@loria.fr, FR</mods:affiliation><wicri:noCountry code="subField">FR</wicri:noCountry></affiliation><affiliation><mods:affiliation>LORIA/CNRS, Campus scientifique, BP 239, 54506 Vandoeuvre-les-Nancy, France; e-mail: {choisy,abelaid}@loria.fr, FR</mods:affiliation><wicri:noCountry code="subField">FR</wicri:noCountry></affiliation>Le document en format XML
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