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IBPRIA
2007
Springer

Handwritten Symbol Recognition by a Boosted Blurred Shape Model with Error Correction

9 years 1 months ago
Handwritten Symbol Recognition by a Boosted Blurred Shape Model with Error Correction
One of the major difficulties of handwriting recognition is the variability among symbols because of the different writer styles. In this paper we introduce the boosting of blurred shape models with error correction, which is a robust approach for describing and recognizing handwritten symbols tolerant to this variability. A symbol is described by a probability density function of blurred shape model that encodes the probability of pixel densities of image regions. Then, to learn the most distinctive features among symbol classes, boosting techniques are used to maximize the separability among the blurred shape models. Finally, the set of binary boosting classifiers is embedded in the framework of Error Correcting Output Codes (ECOC). Our approach has been evaluated in two benchmarking scenarios consisting of handwritten symbols. Compared with state-of-the-art descriptors, our method shows higher tolerance to the irregular deformations induced by handwritten strokes.
Alicia Fornés, Sergio Escalera, Josep Llad&
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where IBPRIA
Authors Alicia Fornés, Sergio Escalera, Josep Lladós, Gemma Sánchez, Petia Radeva, Oriol Pujol
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