Offline handwritten Farsi cursive text recognition using hidden Markov models

Abstract

In this paper we address the problem of recognizing Farsi handwritten words. We extract two types of features from vertical stripes on word images: chain-code of word boundary and distribution of foreground density across the image word. The extracted feature vectors are coded using self organizing vector quantization. The result codes are used for training the model of each word in the database. Each word is modeled using discrete hidden Markov models (HMM). In order to evaluate the performance of the proposed system we conducted an experiment using new prepared database FARSA. We tested the proposed method using 198 word classes in this database. The result of experiment in compare with the existing methods is very promising.