Towards Arabic Handwritten Word Recognition via Probabilistic Graphical Models

Author

Khemiri, Akram ; Kacem, Adel ; Belaid, Abdel

Author_Institution

LaTICE-ESSTT, Univ. of Tunis, Tunis, Tunisia

fYear

2014

fDate

1-4 Sept. 2014

Firstpage

678

Lastpage

683

Abstract

In this work, we propose a novel system for the recognition of handwritten Arabic words. It is evolved based on horizontal-vertical Hidden Markov Model and Dynamic Bayesian Network Model. Our strategy consists of looking for various HMM architectures and selecting those which provide the best recognition performance. Experiments on handwritten Arabic words from IFN/ENIT strongly support the feasibility of the proposed approach. The recognition rates achieve 92.19% with horizontal-vertical Hidden Markov Model and 88.82% with a Dynamic Bayesian Network.

Keywords

belief networks; handwritten character recognition; hidden Markov models; image recognition; natural language processing; Arabic handwritten word recognition; HMM architectures; IFN-ENIT; dynamic Bayesian network model; horizontal-vertical hidden Markov model; probabilistic graphical models; Bayes methods; Computer architecture; Feature extraction; Handwriting recognition; Hidden Markov models; Random variables; Writing; Dynamic Bayesian Network; Feature extraction; Hidden Markov Model; Pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Frontiers in Handwriting Recognition (ICFHR), 2014 14th International Conference on

Conference_Location

Heraklion

ISSN

2167-6445

Print_ISBN

978-1-4799-4335-7

Type

conf

DOI

10.1109/ICFHR.2014.119

Filename

6981098