Abstract
Hidden Markov Models (HMM) have been used with some success in recognising printed Arabic words. In this paper, a complete scheme for unconstrained Arabic handwritten word recognition based on a Multiple discriminant Hidden Markov Models is presented and discussed. The overall engine of this combination of a global feature scheme with an HMM module, is a system able to classify Arabic-Handwritten words and has been tested on one hundred different writers. The system first attempts to remove some of the variation in the images that do not affect the identity of the handwritten word. Next, the system codes the skeleton and edge of the word such that feature information about the strokes in the skeleton is extracted. Then, a classification process based on a rule based classifier is used as that a global recognition engine to classify words into eight groups. Finally, for each group, the HMM approach is used for trial classification. The output is a word in the lexicon. A detailed experiment has been carried out, and successful recognition results are reported.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abuhaiba, S. Mahmoud, and R. Green, Recognition of Handwritten Cursive Arabic Characters, IEEE Trans. Pattern Analysis and Machine Intelligence, June, 1994, 16(6):664–672
H. Alrnuallim and S. Yamaguchi, A method of recognition of Arabic cursive handwriting,, IEEE Trans. Pattern Anal. Machine Intell., 1987, TPAMI(9):715–722.
S. Saadallah and S. Yacu, Design of an Arabic character reading machine, Proc. Of computer Processing of Arabic language, Kuwait, 1985.
Amin, H. Al-Sadoun and S. Fischer, Hand printed character recognition system using artificial network, Pattern recognition, 1996,29(4):663–675.
A.M. Obaid, Arabic handwritten character recognition by neural nets, Journal on communications,,July-Aug, 1994, 45(i):90–91.
A. Saleh, A method of coding Arabic characters and it’s application to context free grammar, Pattern recognition letters, 1994, 15(12)1265–1271.
S. Almaadeed, C. Higgens, and D. Elliman, A New Preprocessing System for the Recognition of Off-line Handwritten Arabic Words, IEEE International Symposium on Signal Processing and Information Technology, December, 2001.
S. Almaadeed, C. Higgens, and D. Ellirnan, a Database for Arabic Handwritten Text Recognition Research, Proc. 8th IWFHR, Ontario, Canada, 2000, (8): 130–135.
D. Guillevic and C. Y. Suen, Recognition of Legal Amount on Bank Cheques, Pattern Analysis & Applic, 1998, 1(1):485–489.
Somaya Alma’adeed, Colin Higgins, and Dave Elliman, Recognition of OffLine Handwritten Arabic Words Using Hidden Marcov Model Approach, ICPR2002, Quebec City, August 2002.
Pavel Pudil, Petr Sornol, and Josef Kittler, Feature Selection in Statistical Pattern Recognition, ICPR 2002 Tutorial, Quebec City, August 2002.
Y. Al-Ohali, M. Cheriet and C. Y. Suen, Databases for Recognition of Handwritten Arabic Cheques, Proc. 7th IWFHR, Amsterdam, the Netherlands, 2000, (7):601–606.
M. Dehghan, K. Faez, M. Ahmadi, M. Shridhar, Handwritten Farsi word recognition: a holistic approach using discrete HMMM, Pattern Recognition, 2001,34(5):1057–1065.
S. Snoussi Maddouri, H. Arniri, Combination of Local and Global Vision Modelling for Arabic Handwritten Words Recognition, Proc. 8th IWFHR, Ontario, Canada, the Netherlands, 2000, 130–135.
M. Khorsheed, A dissertation for the degree of Doctor of philosophy, University of Cambridge, UK, 2002.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag London
About this paper
Cite this paper
Alma’adeed, S., Higgins, C., Elliman, D. (2004). Off-line Recognition of Handwritten Arabic Words Using Multiple Hidden Markov Models. In: Coenen, F., Preece, A., Macintosh, A. (eds) Research and Development in Intelligent Systems XX. SGAI 2003. Springer, London. https://doi.org/10.1007/978-0-85729-412-8_3
Download citation
DOI: https://doi.org/10.1007/978-0-85729-412-8_3
Publisher Name: Springer, London
Print ISBN: 978-1-85233-780-3
Online ISBN: 978-0-85729-412-8
eBook Packages: Springer Book Archive