Application of Hidden Markov Models to Multifont Next Recognition
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Auteurs : J.-C. Anigbogu ; Abdel Belaïd [France]Source :
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Abstract
This paper describes a multifont recognition system that uses Hidden Markov Models. This system works in two phases. In the first phase, it detects the predominant font in the current paragraph and then uses the Modified Viterbi Algorithm to recognize the characters. The choice of the predominant font and the examination of certain features permit a further reduction of the number of models to be traited. The second phase deals with the grouping of characters into words and verification using an application dictionary or digrams. Character recognition results on ten fonts range between 96========percnt; and 99.82========percnt; depending on the font.
Affiliations:
- France
- Grand Est, Lorraine (région)
- 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
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