Z. Imani - Electrical Engineering Department, University of Shahrood, Shahrood, Iran.
Z. Ahmadyfard - Electrical Engineering Department, University of Shahrood, Shahrood, Iran
A. Zohrevand - Computer Engineering & Information Technology Department, University of Shahrood, Shahrood, Iran.

In this paper we address the issue of recognizing Farsi handwritten words. Two types of gradient features are extracted from a sliding vertical stripe which sweeps across a word image. These are directional and intensity gradient features. The feature vector extracted from each stripe is then coded using the Self Organizing Map (SOM). In this method each word is modeled using the discrete Hidden Markov Model (HMM). To evaluate the performance of the proposed method, FARSA dataset has been used. The experimental results show that the proposed system, applying directional gradient features, has achieved the recognition rate of 69.07% and outperformed all other existing methods.

Handwritten word recognition, Directional gradient feature, Hidden Markov Model, Self-organizing feature map, FARSA database