A multi-stream hmm approach to offline handwritten arabic word recognition

TL;DR

This paper introduces a multi-stream Hidden Markov Model approach for offline handwritten Arabic word recognition, utilizing statistical feature extraction and projection methods to improve recognition accuracy.

Contribution

It proposes a novel multi-stream HMM framework combined with specific feature extraction techniques for offline handwritten Arabic recognition.

Findings

Effective feature extraction from handwritten text

Improved recognition accuracy with multi-stream HMM

Applicable to cursive Arabic script

Abstract

In This paper we presented new approach for cursive Arabic text recognition system. The objective is to propose methodology analytical offline recognition of handwritten Arabic for rapid implementation. The first part in the writing recognition system is the preprocessing phase is the preprocessing phase to prepare the data was introduces and extracts a set of simple statistical features by two methods : from a window which is sliding long that text line the right to left and the approach VH2D (consists in projecting every character on the abscissa, on the ordinate and the diagonals 45{\deg} and 135{\deg}) . It then injects the resulting feature vectors to Hidden Markov Model (HMM) and combined the two HMM by multi-stream approach.