Online handwriting Farsi character and number recognition based on hand movement direction using Hidden Markov Models

Online handwriting recognition has many applications and the recognition with high accuracy is essential. In this paper, we introduce a method for online handwriting Farsicharacter and number recognition using Hidden Markov Models (HMM). First we recognize handwriting direction then we getsome statistical and formatting features. The letters are classified by means of these features and then we use HMM for the recognition. We have some movements outside of the main bodyat the beginning or the end letters and number, so in this paper we put a Noise State at the beginning and end hmm and put anaccepting state at the end to increase the recognition accuracy. We use Baum-Welch algorithm to introduce HMM and then givesome samples. Note that the test results demonstrate the scalability of the proposed model and the recognition accuracyfor numbers is 99.22% and for letters is 95.91%.

کلیدواژه ها:

Baum-Welch algorithm ، Forward algorithm ، online handwriting Farsi character and numbers recognition ، Hidden Markov Model (HMM)