Feature transformation with generalized learning vector quantization for hand-written Chinese character recognition
In this paper, the generalized learning vector quantization (GLVQ) algorithm is applied to design a handwritten Chinese character recognition system. The system proposed herein consists of two modules, feature transformation and recognizer. The feature transformation module is designed to extract discriminative features to enhance the recognition performance. The initial feature transformation matrix is obtained by using Fisher's linear discriminant (FLD) function. A template matching with minimum distance criterion recognizer is used and each character is represented by one reference template. These reference templates and the elements of the feature transformation matrix are trained by using the generalized learning vector quantization algorithm. In the experiments, 540100 (5401 x 100) hand-written Chinese character samples are used to build the recognition system and the other 540100(5401 x 100) samples are used to do the open test. A good performance of 92.18% accuracy is achieved by proposed system.
feature transformation generalized learning vector quantization handwritten chinese character recognition fisher's linear discriminant