An improved font recognition method based on texture analysis

Font recognition plays an important role in OCR system. It can be achieved in a simple and effective way with texture analysis by regarding fonts as different textures. But Gabor filters with traditional parameters, which are used to extract features in the previous approach, are not much suitable to font recognition and the RR (Recognition Rate) will decrease sharply when the similar fonts are recognized since the font textures is different from natural textures. Therefore, some adjustments are proposed in this paper to improve the RR: 1. A bank of optimized filters can be gotten by using Genetic Algorithm to optimize the orientation parameters. It can be used to extract distinct features to identify the font well. 2. For reducing the FAR (False-Accept Rate), several dictionaries are set to deal with the diversity in textures of the same font. Experiments are carried out with 899 textures of 4 frequently used Chinese fonts in newspaper, the results compared with the previous show that RR can be improved and the adjustments are useful.