Font identification using the grating cell texture operator
Huanfeng Ma, David Doermann · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
In this paper, a new feature extraction operator, the grating cell operator, is applied to analyze the texture features and classify different fonts of scanned document images. This operator is compared with the isotropic Gabor filter feature extractor which was also employed to classify fonts of documents. In order to improve the performance, a back-propagation neural network (BPNN) classifier is applied to the extracted features to perform the classification and compared with the simple weighted Euclidean distance (WED) classifier. Experimental results 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.