If we ignore the errors between very similar fonts, the recognition rate of about 96.5% will be achieved. This is about 14% better than what an 8-channel Gabor filter can perform. At the same time, SRF can represent the font characteristics very well, so that we achieved the recognition rate of 94.16% on a dataset of 10 popular Farsi fonts. Our experiments show that it is about 50 times faster than an 8-channel Gabor filter. This feature requires much less computation and therefore it can be extracted very faster than common textural features like Gabor filter, wavelet transform or momentum features. Then SRF is extracted as texture features for the recognition. We break each line of text into several small parts and construct a texture. In this paper we perform font recognition in line level using a new feature based on Sobel and Roberts gradients in 16 directions, called SRF. ![]() ![]() On the other hand although the Gabor filter does this task fairly, but it is very time consuming, so that feature extraction of a texture of size 128*128 takes about 178ms on a 2.4GHz PC. Usually all text lines of the same block or paragraph do not have the same font, e.g. Previous methods proposed for font recognition are mostly based on Gabor filters and recognize font type of a block of text rather than a line or a phrase. Font type of individual lines with any font size is recognized based on a new feature. A new approach for the recognition of Farsi fonts is proposed.
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