Binarized Gabor filters based illumination invariant Chinese character recognition

The project of developing an intelligent Chinese chess playing robot in natural environment is facing many challenges in which a robust illumination invariant recognition of the Chinese printed character in a chess piece is addressed in this paper. Instead of illumination sensitive image binarization which inevitably causes information loss and even tends to fail recognition, the proposed approach performs directly Gabor transform on a local subimage which includes Chinese character to obtain directional Gabor filters´ responses which, to some extent, reduce effects of light changes on feature extraction. The responses are binarized by an adaptive thresholding before feature vector which is highly tolerant to illumination changes has been extracted from the binary images. Radial basis function neural network (RBFN) is used to distinguish characters as a classifier. A database consists of 2000 generated Chinese character images with varying intensity and Gaussian noise is used to verify the performance of binarized Gabor filters and Gabor filters respectively. Experiment results reveal that the proposed approach is superior to Gabor filters and a promising 95% recognition rate of characters in natural scenes is observed.