Text Recognition Using Stochastic Models
Identifieur interne : 003408 ( Main/Exploration ); précédent : 003407; suivant : 003409Text Recognition Using Stochastic Models
Auteurs : Abdel Belaïd [France] ; J.-C. AnigboguSource :
English descriptors
Abstract
This paper describes a character recognition system that uses first order Hidden Markov models to treat multifont printed text. The text is first segmented into characters from where features are deduced and quantized. The Modified Viterbi Algorithm is the recognition method chosen. Depending on choice, the system outputs strings of characters, supposedly words, or the string is first matched against a dictionary or the string is passed through a Viterbi net with the output being the recognized word. In tests with deterministic decision trees, the performance is promising in multifont optical character recognition.
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
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Le document en format XML
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