A new method based on bag of filters for character recognition in scene images by learning

Achieving a good recognition rate for scene characters is a big challenge due to non-uniform illumination effects, perspective distortions, multiple colors or contrasts, different fonts and their various sizes, background or orientation variations, etc. Unlike the existing recognition methods that use binary information or the features extracted from different domains, the proposed method explores gray information in the form of a filter bank to extract the discriminative power for all the 62 scene character classes. We propose a sliding window (patch) operation over a character image for learning the global features, which represent the structures of character images of all the classes by reconstructing a filter bank from the original data. We introduce shareable constrains to activate class-specific filters from the filter bank. Further, we propose constraints by studying the nearest neighbor patches and exemplar selection to maximize the gap between inter-classes and minimize the gap between intra-classes. The method is evaluated and compared with several existing recognition methods in terms of character recognition rate. Experimental results show that the proposed method outperforms the existing methods.