Common Techniques and Bio-Inspired Hierarchical Architecture for Automatic Farsi Handwritten Word Recognition Systems
نویسندگان
چکیده
This paper investigates Farsi handwritten word recognition using common features. Also we applied biologically inspired features (BIFs), derived from a feed forward model of object recognition pathway in visual cortex for Farsi handwritten word recognition problem. Experimental results show that the model achieves high recognition percentage even for large variations and applicability of these features in Small Sample Size problems (SSS).The experiments were achieved using the Iranshahr dataset. This dataset consist of 780 samples of 30 city names of Iran which 600 samples for train and 180 samples for test was used. A set of experiments were conducted to compare Decision Templates with some combination rules. Results show that template based fusion method is superior to the other schemes.
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