Illumination invariant character recognition using binarized Gabor features

The project of converting Indian language document to Bharti Braille script has many challenges. The illumination invariant character recognition is one of such challenge which is addressed in this paper The Gabor features provide illumination invariance tip to certain Wend, but in recent developments such as local binary pattern and binarizing the directional filter's response and then computing features from them have made feature highly tolerant to lighting changes. In this context we are proposing the new idea of binarized Gabor feature which is to binarize the Gabor response then compute directional features using a grid structure. To binarize Gabor response we are proposing a threshold such that most vital part Of response is highlighted in its binary form. We are demonstrating the feature extraction techniquefor numeral recognition. The database consists of 1260 scanned numeral images at different scanning parameters and 12000 generated numeral images with varying intensity. The binarized Gabor features are compared with Gabor features based oil classification rates obtained. In all our experimental results better classification rates are observed for the proposed method.