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Highly Accurate Recognition of Handwritten Arabic Decimal Numbers Based on a Self-Organizing Maps Approach



Handwritten numeral recognition is one of the most popular fields of research in automation because it is used in many applications. Indeed, automation has continually received substantial attention from researchers. Therefore, great efforts have been made to devise accurate recognition methods with high recognition ratios. In this paper, we propose a method for integrating the correlation coefficient with a Self-Organizing Maps (SOM)-based technique to recognize offline handwritten Arabic decimal digits. The simulation results show very high recognition rates compared with the rates achieved by other existing methods.



Total Pages: 13
Pages: 493-505


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Volume: 24
Issue: 3
Year: 2018

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S. Abirami and S. Murugappan, (2011). Scripts and Numerals Identification From Printed Multilingual Document Images, Computer Science and Information Technology. 1, 129-146.

F. Ahmed and S. Moskowitz, (2004). Correlation-based watermarking method for image authentication applications, Optical Engineering. 43(8). 1833-1838.

E. Alhoniemi. (2002). Unsupervised pattern recognition methods for exploratory analysis of industrial process data, doctoral thesis, Helsinki University of Technology.

F. Al-Omari and O. Al-Jarrah, (2004). Handwritten Indian Numerals Recognition System Using Probabilistic Neural Networks, Advanced Engineering Informatics. 18(1), 9-16.

A. Alqudah and H. Al-Zoubi, (2015). Efficient k-Class Approach for Face Recognition, Computers and Electrical Engineering. 45, 260-273.

A. Alqudah, H. Al-Zoubi, and M. Al-Khassaweneh, (2012). Shift and Scale Invariant Recognition of Printed Numerals, Journal of Abhath Al-Yarmouk for Basic Science and Engineering. 21(1), 41-49.

H. Al-Zoubi, M. Al-Khassaweneh, and A. Alqudah, (2011). Precise and Accurate Decimal Number Recognition Using Global Motion Estimation, International Journal of Artificial Intelligence and Soft Computing (IJAISC). 2(4), 287-301.

A. Choudhary, R. Rishi, and S. Ahlawat, (2011). Handwritten Numeral Recognition Using Modified BP ANN Structure, Advanced Computing. 133, 56-65.

C. De Stefano, A. Iuliano, and A. Marcelli, (2001). A Shape-Based Algorithm for Detecting Ligatures in On-Line Handwriting, Intelligent Automation & Soft Computing. 7(3), 187-194.

A. Goltsev and D. Rachkovskij, (2005). Combination of the Assembly Neural Network with a Perceptron for Recognition of Handwritten Digits Arranged in Numeral Strings, The Journal of the Pattern Recognition Society. 38(3), 315-322.

E. Gómez, Y.A. Dimitriadis, M. Sánchez-Reyes Más, P. Sánchez Gracía, J.M. Cano Izquierdo and J. López Coronado, (2001). On-Line Character Analysis and Recognition With Fuzzy Neural Networks, Intelligent Automation & Soft Computing. 7(3), 163-175.

S. Kaski, S., J. Kangas, and T. Kohonen, (1998). Bibliography of self-organizing map (SOM) papers: 1981-1997, Neural Computing Surveys. 1, 102-350.

K. El Hindi, M. Khayyat and A. Abu Kar, (2017). Comparing the Machine Ability to Recognize Hand-Written Hindu and Arabic Digits, Intelligent Automation & Soft Computing. 23(2), 295-301.

M. Kherallah, L. Haddad, A. Alimi, and A. Mitiche, (2008). On-line Handwritten Digit Recognition Based on Trajectory and Velocity Modeling, Pattern Recognition Letters. 29(5), 580-594.

Kohonen, Teuvo. "SELF-ORGANIZING MAPS: OPHMIZATION APPROACHES." Artificial Neural Networks (1991): 981-990. Crossref. Web.

Kohonen, T. "Things You Haven”t Heard About the Self-Organizing Map." IEEE International Conference on Neural Networks n. pag. Crossref. Web.

Kohonen, Teuvo. "Self-Organizing Maps." Springer Series in Information Sciences (1997): n. pag. Crossref. Web.

T. Kohonen, E. Oja, O. Simula, A. Visa, and J. Kangas, (1996). Engineering applications of the self-organizing map, Proc. of the IEEE 84. 10, 1358-1384.

E. Kussul and T. Baidyk, (2004). Improved Method of Handwritten digit Recognition Tested on MNIST Database, Image and Vision Computing. 22(12), 971-981.

F. Lauer, C. Suen, and G. Bloch, (2007). A Trainable Feature Extractor for Handwritten Digit Recognition, The Journal of the Pattern Recognition Society. 40(6), 1816-1824.

X. Li, Y. Sun, M. Tang, X. Yan, and Y. Kang, (2011). A Neural Network-Based Intelligent Image Target Identification Method and Its Performance Analysis, Intelligent Automation & Soft Computing. 17(7), 885-896.

C.-L. Liu and H. Sako, (2006). Class-Specific Feature Polynomial Classifier for Pattern Classification and its Application to Handwritten Numeral Recognition, The Journal of the Pattern Recognition Society. 39(4), 669-681.

Y.-C. Liu, C. Wu, and M. Liu, (2011). Research of fast SOM clustering for text information, Expert Systems with Applications. 38(8), 9325-9333.

S. Mahmoud, (2008). Recognition of Writer-Independent Off-Line Handwritten Arabic (Indian) Numerals Using Hidden Markov Models, Signal Processing. 88(4), 844-857.

T. E. Milson and K. R. Rao, (1976). A Statistical Model for Machine Print Recognition, IEEE Transactions on Systems, Man, and Cybernetics, SMC 6. 10, 671-678.

M. A. Narasimhan, V. Devarajan, and K. R. Rao, (1980). Simulation of Alphanumeric Machine Print Recognition, IEEE Transactions on Systems, Man, and Cybernetics, SMC l0. 5, 270-275.

A. D. Parkins and A. K. Nandi, (2004). Genetic Programming Techniques for Hand Written Digit Recognition, The Journal of Signal Processing 84. 12, 2345-2365.

X. Peng and D. Xu, (2012). Twin Mahalanobis distance-based support vector machines for pattern recognition, Information Sciences. 200, 22-37.

R. Plamondon and S. N. Srihari, (2000). On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey, IEEE Transactions on Pattern Analysis and Machine Intelligence. 22(1), 63-84.

G. R. Rajput, R. Horakeri, and S. Chandrakant, (2010). Printed and Handwritten Kannada Numeral Recognition Using Crack Codes and Fourier Descriptors Plate, International Journal of Computer Applications. 1(1), 53-58.

F. Ren, and M. Sohrab, (2013). Class-indexing-based term weighting for automatic text classification, Information Sciences. 236, 109-125.

Said, F.N., A. Yacoub, and C.Y. Suen. "Recognition of English and Arabic Numerals Using a Dynamic Number of Hidden Neurons." Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR ”99 (Cat. No.PR00318) (1999): n. pag. Crossref. Web.

J. Sas and U. Markowska-Kaczmar, (2012). Similarity-based training set acquisition for continuous handwriting recognition, Information Sciences. 191, 226-244.

O. Simula and J. Kangas, (1995). Process monitoring and visualization using self organizing maps, In A. B. Bulsari, editor, Neural Networks for Chemical Engineers. 371-384.

H. Soltanzadeh and M. Rahmati, (2004). Recognition of Persian handwritten digits using image profiles of multiple orientations, Pattern Recognition Letters. 25(14), 1569-1576.

S. Sun, H. Park, D. R. Haynor, and Y. Kim, (2003). Fast template matching using correlation-based adaptive predictive search, International Journal of Imaging Systems and Technology. 13(3), 169 - 178.

J. Vesanto, J. Himberg, E. Alhoniemi, and J. Parhankangas, (2000). SOM Toolbox for Matlab 5, Tech. Rep. A57, Helsinki University of Technology.

H. Zhao and C. Kit, (2011). Integrating unsupervised and supervised word segmentation: The role of goodness measures, Information Sciences. 181(1), 163-183.


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present


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