Font identification using the grating cell texture operator

In this paper, a new feature extraction operator, the grating cell operator, is applied to analyze the texture features and classify fonts of scanned document images. This operator is compared with the isotropic Gabor filter which was also employed for font classification. In order to improve the performance, a back-propagation neural network (BPNN) classifier was applied and compared with the simple weighted Euclidean distance (WED) classifier. Experimental results for five fonts of three scripts show that the grating cell operator performs better than the isotropic Gabor filter, and the BPNN classifier can provide more accurate classification results than the WED classifier.