文章基本信息

标题：Common Techniques and Bio-Inspired Hierarchical Architecture for Automatic Farsi Handwritten Word Recognition Systems
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作者：Reza Ebrahimpour ; Mona Amini ; Afra Vahidi Shams 等
期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2011
卷号：4
期号：3
出版社：SERSC
摘要：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
关键词：Farsi handwritten word recognition; Feature extraction; Biologically inspired ;features; Human visual pathway