Confidence Measures in Recognizing Handwritten Mathematical Symbols
Calculemus '09/MKM '09 Proceedings of the 16th Symposium, 8th International Conference. Held as Part of CICM '09 on Intelligent Computer Mathematics
Toward affine recognition of handwritten mathematical characters
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Improved classification through runoff elections
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Polynomial approximation in handwriting recognition
Proceedings of the 2011 International Workshop on Symbolic-Numeric Computation
HBF49 feature set: A first unified baseline for online symbol recognition
Pattern Recognition
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We propose an efficient method to recognize multi-stroke handwritten symbols. The method is based on computing the truncated Legendre-Sobolev expansions of the coordinate functions of the stroke curves and classifying them using linear support vector machines. Earlier work has demonstrated the efficiency and robustness of this approach in the case of single-stroke characters. Here we show that the method can be successfully applied to multi-stroke characters by joining the strokes and including the number of strokes in the feature vector or in the class labels. Our experiments yield an error rate of 11-20%, and in 99% of cases the correct class is among the top 4. The recognition process causes virtually no delay, because computation of Legendre-Sobolev expansions and SVM classification proceed on-line, as the strokes are written.