Dynamic recognition in the omni-writer frame: Application to hand-printed text recognition

Oudot, Loïc; Prévost, Lionel; Milgram, Maurice

doi:10.1109/icdar.2001.953943

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…The text to recognized is represented by sequences of (x,y) coordinates of the pen moving. Our recognition system [5] consists of three subsets of experts (figure 1).…”

Section: Baseline Recognition Systemmentioning

confidence: 99%

Self-supervised writer adaptation using perceptive concepts : application to on-line text recognition

Oudot¹,

Prevost²,

Moises³

et al. 2004

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.

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We recently designed a hand-printed text recognizer. The system is based on three set of experts respectively used to segment, classify and validate the text (with a French lexicon : 200K words). We present in this communication writer adaptation methods. The first is supervised by the user. The others are self-supervised strategies which compare classification hypothesis with lexical hypothesis and modify consequently classifier parameters. The last method increases the system accuracy and the classification speed. Experiments are presented on a large database of 90 texts (5400 words) written by 54 different writers and good recognition rates (82%) have been obtained.

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“…The text to recognized is represented by sequences of (x,y) coordinates of the pen moving. Our recognition system [5] consists of three subsets of experts (figure 1).…”

Section: Baseline Recognition Systemmentioning

confidence: 99%

Self-supervised writer adaptation using perceptive concepts : application to on-line text recognition

Oudot¹,

Prevost²,

Moises³

et al. 2004

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.

Self Cite

View full text Add to dashboard Cite

show abstract

“…We have recently designed a handwriting text recognizer [5] that should be implemented on personal digital assistants or other hand-held devices. For such an application, recognition rates should be very high otherwise it should discourage all the possible users.…”

Section: Introductionmentioning

confidence: 99%

Combining model-based and discriminative classifiers: application to handwritten character recognition

Prévost¹,

Michel-Sendis²,

Moises³

et al.

Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.

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Handwriting recognition is such a complex classification problem that it is quite usual now to make co-operate several classification methods at the preprocessing stage or at the classification stage. In this paper, we present an original two stages recognizer. The first stage is a model-based classifier that stores an exhaustive set of character models. The second stage is a discriminative classifier that separates the most ambiguous pairs of classes. This hybrid architecture is based on the idea that the correct class almost systematically belongs to the two more relevant classes found by the first classifier. Experiments on Unipen database show a 30% improvement on a 62 classes recognition problem.

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Dynamic recognition in the omni-writer frame: Application to hand-printed text recognition

Cited by 2 publications

References 5 publications

Self-supervised writer adaptation using perceptive concepts : application to on-line text recognition

Self-supervised writer adaptation using perceptive concepts : application to on-line text recognition

Combining model-based and discriminative classifiers: application to handwritten character recognition

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