Symbol Recognition: Current Advances and Perspectives
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECOC-ONE: A Novel Coding and Decoding Strategy
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Primitive segmentation in old handwritten music scores
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
Report on the Third Contest on Symbol Recognition
Graphics Recognition. Recent Advances and New Opportunities
Multi-class Binary Symbol Classification with Circular Blurred Shape Models
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
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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.