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.
KeywordsZernike Moment Multiclass Problem Symbol Recognition Handwritten Document Shape Point
Unable to display preview. Download preview PDF.
- 4.Torralba, A., Murphy, K., Freeman, W.: Sharing visual features for multiclass and multiview object detection. Technical Report, Massachusetts Institute of Technology Computer Science and Artificial Intelligence, MIT AIM (2004)Google Scholar
- 5.Escalera, S., Pujol, O., Radeva, P.: ECOC-ONE: A Novel Coding and Decoding Strategy. In: International Conference on Pattern Recognition (ICPR), Hong Kong, vol. 3, pp. 578–581 (2006)Google Scholar
- 9.Manjunath, B., Salembier, P., Sikora, T.: Introduction to mpeg-7. In: Multimedia content description interface, John Wiley and Sons, Chichester (2002)Google Scholar
- 10.Kim, W.: A new region-based shape descriptor. Technical report, Hanyang University and Konan Technology (1999)Google Scholar