Handwritten Arabic (Indian) Numerals Recognition using Expert System

Fahad Layth MALALLAH; Asem KHMAG; Ayad HUSSAIN; Zaid Ahmed ALJAWARYY

Authors

Fahad Layth MALALLAH Faculty of Computer Science, Cihan University of Sulaimaniya, Kurdistan
Asem KHMAG Department of Computer Systems Engineering, Faculty of Engineering, University of Zawia, Az zawia
Ayad HUSSAIN Faculty of Computer Science, Mosul University, Mosul
Zaid Ahmed ALJAWARYY Faculty of Science and Technology, University of Human Development of Sulaimaniya, Kurdistan Region

Keywords:

Arabic numeral character recognition, image processing, pattern recognition, feature extraction, object segmentation, expert system

Abstract

The capability of a computer system to interpret intelligible handwritten data input and analyze it for many automated process system is the core of a handwriting character recognition system. Handwritten recognition plays a major role in many applications, such as cheques verification, office automation, natural human-computer interaction, as well as mail sorting. Procedures of handwritten numeral recognition have showed a remarkable contribution in order to improve the automation process and have developed human and machine interaction in several fields and applications. In this paper, the proposed approach is based on extraction of both local and global geometric features. The features are further quantified into a set of facts or conditions that are used for classification based on a set of rules as an expert system. In addition, sample handwritten images are tested with the proposed technique and the results are stated and accuracy rate is calculated based on a confusion matrix. The output showed that the recognition error rate in terms of False Rejection Rate (FRR) is 4 % or the overall successful accuracy is 96 %.

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References

R Palacios, A Gupta and PS Wang. Handwritten bank check recognition of courtesy amounts. Int. J. Image Graph. 2004; 4, 203-22.

W Ding, CY Suen and A Krzyżak. A new courtesy amount recognition module of a check reading system. In: Proceeding of the 19th International Conference in Pattern Recognition. Tampa, USA, 2008, p. 1-4.

M Wienecke, GA Fink and G Sagerer. Toward automatic video-based whiteboard reading. Int. J. Doc. Anal. Recogn. 2005; 7, 188-200.

B Al-Badr and SA Mahmoud. Survey and bibliography of Arabic optical text recognition. Signal Process. 1995; 41, 49-77.

D Trier, AK Jain and T Taxt. Feature extraction methods for character recognition-a survey. Pattern Recogn. 1996; 29, 641-62.

J Pradeep, E Srinivasan and S Himavathi. Diagonal based feature extraction for handwritten alphabets recognition system using neural network. In: Proceeding of the 3rd International Conference on Electronics Computer Technology. Kanyakumari, India, 2011.

L Ferrer, E Shriberg, S Kajarekar and K Sonmez. Parameterization of prosodic feature distributions for SVM modeling in speaker recognition. In: Proceeding of the IEEE International Conference in Acoustics, Speech and Signal Processing. Honolulu, USA, 2007.

A Khmag, AR Ramli, SJB Hashim and SAR Al-Haddad. Additive noise reduction in natural images using second-generation wavelet transform hidden Markov models. IEEE J. Trans. Electr. Electron. Eng. 2016; 11, 339-47.

CL Liu, K Nakashima, H Sako and H Fujisawa. Handwritten digit recognition using state-of-the-art techniques. In: Proceeding of the 8th International Workshop in Frontiers in Handwriting Recognition. Ontario, Canada, 2002, p. 320-5.

MH Shirali-Shareza, K Faez and A Khotanzad. Recognition of handwritten farsi numerals by zernike moments features and a set of class specific neural network classifiers. In: Proceeding of the International Conference on Signal Processing Applications, and Technology. Dallas, Texas, 1994, p. 998-1003.

MH Shirali-Shahreza, K Faez and A Khotanzad. Recognition of handwritten Persian/Arabic numerals by shadow coding and an edited probabilistic neural network. In: Proceeding of the International Conference on Image Processing. Washington DC, 1995, p. 436-9.

H Almuallim and S Yamaguchi. A method of recognition of Arabic cursive handwriting. IEEE Trans. Pattern Anal. Mach. Intell. 1987; 9, 715-22.

A Amin and HB Al-Sadoun. A new segmentation technique of Arabic text. In: Proceeding of the 11th International Conference on Pattern Recognition Methodology and Systems. The Hague, 1992, p. 441-5.

A Amin and HB Al-Sadoun. Hand printed Arabic character recognition system. In: Proceeding of the 12th Conference on IAPR International. Jerusalem, 1994, p. 536-9.

I Abuhaiba, MJ Holt and S Datta. Processing of binary images of handwritten text documents. Pattern Recogn. 1996; 29, 1161-77.

S Saadallah and S Yacu. Design of an Arabic character reading machine. In: Proceeding of the Computer Processing of Arabic Language. Kuwait, 1985.

A Saleh. A method of coding hand-written Arabic characters and its application to context-free grammar. Pattern Recogn. Lett. 1994; 15, 1265-71.

IS Abuhaiba, MJ Holt and S Datta. Recognition of off-line cursive handwriting. Comput. Vis. Image Understand. 1998; 71, 19-38.

MS Khorsheed. Recognising handwritten Arabic manuscripts using a single hidden Markov model. Pattern Recogn. Lett. 2003; 24, 2235-42.

S Almaadeed, C Higgins and D Elliman. Off-line recognition of handwritten Arabic words using multiple hidden Markov models. Knowl. Base. Syst. 2004; 17, 75-9.

HA Al-Muhtaseb, SA Mahmoud and RS Qahwaji. Recognition of off-line printed Arabic text using Hidden Markov Models. Signal Process. 2008; 88, 2902-12.

H Bunke, S Bengio and A Vinciarelli. Offline recognition of unconstrained handwritten texts using HMMs and statistical language models. IEEE Trans. Pattern Anal. Mach. Intell. 2004; 26, 709-20.

Y Kessentini, T Paquet and AB Hamadou. A multi-stream HMM-based approach for off-line multi-script handwritten word recognition. In: Proceeding of the 11th International Conference on Frontiers in Handwriting Recognition. Montreal, Quebec, Canada, 2008, p. 147-52.

E El-Alfy and RE Abdel-Aal. Using GMDH-based networks for improved spam detection and email feature analysis. Appl. Soft Comput. 2011; 11, 477-88.

R Fox and W Hartmann. An abductive approach to hand-written character recognition for multiple domains. In: Proceeding of the International Conference on Artificial Intelligence. CSREA Press, 2006, p. 349-55.

T Plötz and GA Fink. Markov models for offline handwriting recognition: A survey. Int. J. Doc. Anal. Recogn. 2009; 12, 269-98.

A Amin and JF Mar. Machine recognition and correction of printed Arabic text. IEEE Trans. Syst. Man Cybern. 1989; 19, 1300-6.

LD Harmon. Automatic recognition of print and script. Proc. IEEE 1972; 60, 1165-76.

FN Said. 1996, Recognition of Symbols and Characters on Engineering Drawings and Maps. Ph.D Dissertation, Concordia University, Canada.

N Indurkhya and SM Weiss. Heuristic configuration of hidden units, in backpropagation neural networks. In: Proceeding of the International Joint Conference on Neural Networks. Seattle, WA, 1991.

M Gardner and S Dorling. Artificial neural networks (the multilayer perceptron): A review of applications in the atmospheric sciences. Atmos. Environ. 1998; 32, 2627-36.

YL Cun and Y Bengio. Convolutional Networks for Images, Speech, and Time Series. The Hand Book of Brain Theory and Neural Networks. MIT Press Cambridge, MA, USA, 1998, p. 255-8.

F Al-Omari. Handwritten Indian numeral recognition system using template matching approaches. In: Proceeding of the IEEE International Conference on Computer Systems and Applications. Beirut, Lebanon, 2001, p. 83-8.

J Sadri, CY Suen and TD Bui. Application of support vector machines for recognition of handwritten Arabic/Persian digits. In: Proceeding of the 2nd Iranian Conference on Machine Vision and Image Processing. Tehran, Iran, 2003, p. 300-7.

J Arlandis, JC Perez-Cortes and J Cano. Rejection strategies and confidence measures for a k-NN classifier in an OCR task. In: Proceeding of the 16th International Conference on Pattern Recognition. Quebec, Canada, 2002, p. 576-9.

JA Rodrıguez and F Perronnin. Local gradient histogram features for word spotting in unconstrained handwritten documents. In: Proceeding of the International Conference on Frontiers in Handwriting Recognition. Montréal, Québec, Canada, 2008.

RM Haralock and LG Shapiro. Computer and Robot Vision. Addison-Wesley Longman Publishing, Boston, USA, 1992.

P Jackson. Introduction to Expert Systems. Addison-Wesley Longman Publishing, Boston, USA, 1986.

M Ahmed and RK Ward. An expert system for general symbol recognition. Pattern Recogn. 2000; 33, 1975-88.

M Negnevitsky. Artificial Intelligence: A Guide to Intelligent Systems. Addison-Wesley, Boston, USA, 2005.

M Sasikumar, S Ramani, M Raman, KSR Anjaneyulu and R Chandrasekar. A Practical Introduction to Rule Based Expert Systems. Narosa Publishing House, New Delhi, India, 2007.

T Sharma, N Tiwari and D Kelkar. Study of difference between forward and backward reasoning. Int. J. Emerg. Tech. Adv. Eng. 2012; 2, 271-3.

LG Shapiro and RM Haralick. Computer and Robot Vision. Vol I. Addison-Wesley, USA, 1992.

TY Kong and A Rosenfeld. Topological Algorithms for Digital Image Processing. Vol I. Elsevier, North Holland, 1996.

F Malallah, SMS Ahmad, WAW Adnan, OA Arigbabu, V Iranmanesh and S Yussof. Online handwritten signature recognition by length normalization using up-sampling and down-sampling. Int. J. Cyber Secur. Digit. Foren. 2015; 4, 302-13.

F Malallah, SMS Ahmad, WAW Adnan and S Yussof. Non-invertible online signature biometric template protection via shuffling and trigonometry transformation. Int. J. Comput. Appl. 2014; 98, 4-17.