High Accuracy Handwritten Chinese Character Recognition Based on Support Vector Machine and Independent Component Analysis
This paper proposed a new method for handwritten Chinese character recognition based on a combination of independent component analysis (ICA) and support vector machine (SVM). First, we extracted independent basis images of handwritten Chinese character image and the projection vector by using fast ICA algorithm, and obtained the feature vector. Then, we used two stage classification methods based on SVM for classification. The scheme took full advantage of good extraction local features capability of ICA and strong classification ability of SVM, thus increasing the system’s recognition rate. The experiments show that the feature extraction method based on ICA is superior to that of gradient-based, and the two stage classifiers based on SVM is better than that of modified quadratic discriminant function. On HCL2000, a handwritten Chinese character database, the recognition accuracy of 99.87 % has been achieved.
KeywordsHandwritten chinese character recognition Independent component analysis Support vector machine Feature extraction
The work was partially supported by Key Projects of Education Department of Sichuan Province under Grant No. 10ZA186.
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