Abstract
A new hybrid system for word recognition is discussed in this work. The system is based on a modification to the view-based approach presented in authors’ previous works. The system does not need thinning or segmentation of the analyzed word. The word is treated as a whole image. The characteristic vectors taken from both top and bottom views of the image are processed with the method of minimal eigenvalues of Töeplitz matrices. The obtained series of minimal eigenvalues are used for classification with Artificial Neural Networks. The results of the experiments on a set of common English words are presented.
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References
Tabedzki, M., Saeed, K.: View-Based Word Recognition System. ICNNAI’2006. 31 May - 2 June, 2006, Brest, Belarus, pp. 145-148.
Tabedzki, M., Saeed, K.: A View-Based Töeplitz-Matrix-Supported System for Word Recognition without Segmentation. ISDA’2006. October 16-18, 2006, China.
Burr, D.J.: Experiments on Neural Net Recognition of Spoken and Written Text. IEEE Trans. on Acoustic, Speech and Signal Processing, Vol. 36. 1988, pp. 1162-1168
Cheung, A., Bennamoun, M., Bergman, N. W.: An Arabic Optical Character Recognition System Using Recognition-Based Segmentation. Pattern Recognition, Vol. 34, No. 2. 2001, 215-233.
Amin, A.: Off-Line Arabic Character recognition: the-state-of-art. Pattern Recognition, Vol. 31, No. 5. 1998, pp. 517-530.
Govindan, V. K., Shivaprasad, A. P.: Character Recognition – Review. Pattern Recognition, Vol. 23, No. 7. 1990, pp. 671-683.
Jain, A. K., Duin, R. P. W., Mao, J.: Statistical Pattern Recognition: A Review. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, 2000, pp. 4-37.
Gonzalez, R. C., Woods, R. E.: Digital Image Processing. Addison-Wesley Publishing Company, 1992, pp. 7-9, pp. 413-414.
Amin, A., Mari, J.: Machine Recognition and Correction of Printed Arabic Text. IEEE Trans. on Systems Man, and Cybernetics, Vol. SMC-19, No. 5. 1989, pp. 1300-1306.
El-Sheikh, T. S., Guindi, R.: Computer recognition of Arabic cursive scripts. Pattern Recognition, Vol. 21, No. 4. 1988, pp. 293-302.
Rybnik, M., Chebira, A., Madani, K., Saeed, K., Tabedzki, M., Adamski, M.: A Hybrid Neural-Based Information-Processing Approach Combining a View-Based Feature Extractor and a Treelike Intelligent Classifier. CISIM – Computer Information Systems and Industrial Management Applications. WSFiZ Press, Bialystok 2003, pp. 66-73.
Saeed, K., Tabedzki, M.: A New Hybrid System for Recognition of Handwritten-Script. COMPUTING – International Scientific Journal of Computing. Institute of Computer Information Technologies, Volume 3, Issue 1, Ternopil 2004, pp. 50-57.
Saeed, K.: A New Approach in Image Classification. Proc. 5 th International Conference on Digital Signal Processing and its Applications – DSPA’03. Moscow 2003. Vol. 1, pp. 49-52.
Saeed, K., Tabedzki, M.: Cursive-Character Script Recognition using Töeplitz Model and Neural Networks. L. Rutkowski, J. Siekmann, R. Tadeusiewicz, L. Zadeh (Eds), Lecture Notes in Computer Science – LNCS 3070. Springer-Verlag, Heidelberg 2004, pp.658-663.
Haykin, S.: Neural Networks. A Comprehensive Foundation. Prentice Hall. New Jersey, USA, second edition, 1999.
http://www.world-english.org
Saeed, K., Tabedzki, M., Adamski, M.: A New View-Based Approach for object Recognition. CONRADI, Research Review, Vol. 2, No. 1. University of Vaasa, Turku 2003, pp. 80-90.
Saeed, K.: On The Realization of Digital Filters. Proc. The 1 st International Conference on Digital Signal Processing and its Applications – DSPA’98. Vol. 1, Moscow, 1998, pp. 141-143.
Shakhnarovish, G., Darrell, T., Indyk, P.: Nearest-Neighbor Methods in Learning and Vision. The MIT Press, 2005.
Kolmogorov, A. N.: On the Representation of Continuous Functions of Several Variables by Superposition of Continuous Functions of One Variable and Addition. Dokl. Akad. Nauk SSSR, 1957, vol. 114, pp. 953-956.
Guillevic, D., Suen, C. Y.: HMM Word Recognition Engine. Fourth International Conference on Document Analysis and Recognition – ICDAR97. 1997, pp. 544-547.
De Oliveira, J. J. Jr., De Carvalho, J. M., De A. Freitas, C. O., Sabourin, R.: Evaluating NN and HMM Classifiers for Handwritten Word Recognition. Computer Graphics and Image Processing. Elect. Engg Dept., Fed. Univ. of Campina Grande, Brazil, 2002, pp. 210-217.
Lavrenko, V., Rath, T. M., Manmatha, R.: Holistic word recognition for handwritten historical documents. Document Image Analysis for Libraries. 2004, pp. 278-287.
Saeed, K., Nammous, M.: A Speech-and-Speaker Identification System – Feature Extraction, Description, and Classification of Speech-Signal Image. Accepted for publication in IEEE Trans. on Ind. Electronics – Humatronics. New York, USA, 2006.
Tabedzki, M., Saeed, K.: Modified Word Recognition System – New Performance. Accepted for publication in IEEE-CISIM. 2007.
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Saeed, K., Tabedzki, M. (2007). New Experiments on Word Recognition Without Segmentation. In: Pejaś, J., Saeed, K. (eds) Advances in Information Processing and Protection. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-73137-7_28
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DOI: https://doi.org/10.1007/978-0-387-73137-7_28
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