Discrimination of similar handwritten numerals based on invariant curvature features

Authors:
Lihua Yang;Ching Y. Suen;Tien D. Bui;Ping Zhang
Affiliations:
School of Mathematics and Computing Science, Sun Yat-sen University, Guangzhou city 510275, P.R. China and Center for Pattern Recognition and Machine Intelligence, Concordia University, Montreal, ...;Center for Pattern Recognition and Machine Intelligence, Concordia University, Montreal, Canada H3G 1M8;Center for Pattern Recognition and Machine Intelligence, Concordia University, Montreal, Canada H3G 1M8;Center for Pattern Recognition and Machine Intelligence, Concordia University, Montreal, Canada H3G 1M8
Venue:
Pattern Recognition
Year:
2005

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Abstract

This paper studies the discrimination of similar handwritten numerals based on invariant curvature features. High-order B-splines are used to calculate the curvature of the contours of handwritten numerals. The concept of a distribution center is introduced so that a one-dimensional periodic signal can be normalized as shift invariant. Consequently, the curvature of the contour of a character becomes rotation invariant. To reduce the dimension of the features, wavelet basis decomposition is used to produce more compact features. Finally, artificial neural network (ANN) and support vector machines (SVM) are employed to train the features and design classifiers of high recognition rates.