Cursive Arabic Handwriting Recognition System Without Explicit Segmentation Based on Hidden Markov Models

Mouhcine Rabi,Zouhair Mahani,Mustapha Amrouch

doi:10.46298/jdmdh.3143

Abstract

In this paper we present a system for offline recognition cursive Arabic handwritten text which is analytical without explicit segmentation based on Hidden Markov Models (HMMs). Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution characteristics in the word image. These features are modelled using hidden Markov models. The HMM-based classifiercontains a training module and a recognition module. The training module estimates the parameters of each of the character HMMs uses the Baum-Welchalgorithm. In the recognition phase, feature vectors extracted from an image are passed to a network of word lexicon entries formed of character models. The character sequence providing the maximumlikelihood identifies the recognized entry. If required, the recognition can generate N best output hypotheses rather than just the single best one. To determine the best output hypotheses, the Viterbi algorithm is used.The experiments on images of the benchmark IFN/ENIT database show that the proposed system improves recognition.

Highlights

The recognition of cursive Arabic handwriting is an active area of pattern recognition research
The subject of this article concerns the recognition of cursiveArabic handwriting [M.T Parvez 2013] [AL-Shatnawi 2011].Several systems are available based on two approaches; a globalapproach that considers the word as non-divisible base entityavoiding the segmentation process and its problems.This approach is reliable and applicable for vocabularies oflimited size
In order to investigate the potential of our systemfor offline cursive handwriting recognition, the benchmarkdatabase IFN/ENIT is used [Peschwitz 2003], that contains atotal of 26459 handwritten words of 946 Tunisian town/villagesnames written by different writers.We used http://jdmdh.episciences.org the toolbox HTK (Hidden Markov Model Toolkit[S.Young 2006]) to model the characters and words

Summary

INTRODUCTION

The recognition of cursive Arabic handwriting is an active area of pattern recognition research. The analytical approach is based on thedecomposition of the word sequence into characters orgraphemes proceeding by a segmentation phase. The latter canbe explicitly based on a priori division of the image into subunits(letters or grapheme) or implicitly based on a recognitionengine to validate and rank the segmentation hypothesis.The approach used in our system is analytical based on implicitsegmentation; segmentation and recognition are carried outjointly. The first step of a handwriting recognition system afterpreprocessing is the extraction features. Section 2presents a detailed description of the features extraction precededby baselines estimation.

Baseline Estimation

EXPERIMENTATIONS AND RESULTS

CONCLUSION & PERSPECTIVES