Abstract
Optical character recognition of cursive scripts present a number of challenging problems in both segmentation and recognition processes and this attracts many researches in the field of machine learning. This paper presents a novel approach based on a combination of MLP and SVM to design a trainable OCR for Persian/Arabic cursive documents. The implementation results on a comprehensive database show a high degree of accuracy which meets the requirements of commercial use.
Keywords
- Support Vector Machine
- Natural Language Processing
- Fuzzy Inference System
- Support Vector Machine Classifier
- Multi Layer Perceptrons
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Amin, A.: Off line Arabic character recognition - a survey. In: Proceedings of the International Conference on Document Analysis and Recognition, vol. 2, pp. 596–599 (1997)
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)
Al-Badr, B., Haralick, R.M.: Segmentation-free word recognition with application to Arabic. In: Proceedings of the Third International Conference on Document Analysis and Recognition, vol. 1, pp. 355–359. IEEE Comput. Soc. Press, Los Alamitos (1995)
Bazzi, I., Schwartz, R., Makhoul, J.: An omnifont open-vocabulary OCR system for English and Arabic. IEEE Transactions on Pattern Analysis & Machine Intelligence 21(6), 495–504 (1999)
Hassin, A.H., Xiang-Long, T., Jia-Feng, L., Wei, Z.: Printed Arabic character recognition using HMM. Journal of Computer Science & Technology 19(4), 538–543 (2004)
Cheung, A., Bennamoun, M., Bergmann, N.W.: An Arabic optical character recognition system using recognition-based segmentation. Pattern Recognition 34(2), 215–233 (2001)
Weissman, H., Schenkel, M., Guyon, I., Nohl, C., Henderson, D.: Recognition-based segmentation of on-line run-on handprinted words: input vs. output segmentation. Pattern Recognition 27(3), 405–420 (1994)
Azmi, R., Kabir, E.: A new segmentation technique for ominifont farsi text. Pattern Recognition Letters 22(2), 97–104 (2001)
Haykin, S.: Adaptive Filter Theory, 3rd edn. Prentice-Hall, Upper Saddle River (1996)
Kavianifar, M., Amin, A.: Preprocessing and structural feature extraction for a multi-fonts Arabic/Persian OCR. In: Conference on Document Analysis and Recognition, pp. 213–216. IEEE Computer Soc., Los Alamitos (1999)
Kurdy, B.M., AlSabbagh, M.M.: Omnifont Arabic optical character recognition system. In: Proceedings of Int. Conf. on Information and Communication Technologies: From Theory to Applications, pp. 469–470. IEEE, Piscataway (2004)
Pirsiavash, H., Razzazi, F.: Design and Implementation of a Hierarchical Classifier for Isolated Handwritten Persian/Arabic Characters. In: IJCI Proceedings of International Conference on Signal Processing, Turkey, vol. 1(2) (September 2003)
Hu, M.K.: Visual Pattern Recognition by Moment Invariants. IEEE Transactions on Information Theory IT-8, 179–187 (1962)
Gonzalez, R.C., Wintz, P.: Digital Image Processing, 2nd edn., pp. 392–423. Addison Wesly Publishing Company, London (1987)
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Pirsiavash, H., Mehran, R., Razzazi, F. (2005). A Robust Free Size OCR for Omni-Font Persian/Arabic Printed Document Using Combined MLP/SVM. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_63
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DOI: https://doi.org/10.1007/11578079_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
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