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A Neural Approach to Cursive Handwritten Character Recognition Using Features Extracted from Binarization Technique

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Complex System Modelling and Control Through Intelligent Soft Computations

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 319))

Abstract

The feature extraction is one of the most crucial steps for an Optical Character Recognition (OCR) System. The efficiency and accuracy of the OCR System, in recognizing the off-line printed characters, mainly depends on the selection of feature extraction technique and the classification algorithm employed. This chapter focuses on the recognition of handwritten characters of Roman Script by using features which are obtained by using binarization technique. The goal of binarization is to minimize the unwanted information present in the image while protecting the useful information. Various preprocessing techniques such as thinning, foreground and background noise removal, cropping and size normalization etc. are also employed to preprocess the character images before their classification. A multi-layered feed forward neural network is proposed for classification of handwritten character images. The difference between the desired and actual output is calculated for each cycle and the weights are adjusted during error back-propagation. This process continues till the network converges to the allowable or acceptable error. This method involves the back propagation-learning rule based on the principle of gradient descent along the error surface in the negative direction. Very promising results are achieved when binarization features and the multilayer feed forward neural network classifier is used to recognize the off-line cursive handwritten characters.

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References

  • Aburas, A. A., & Rehiel, S. A. (2008). New promising off-line tool for Arabic handwritten character recognition based on JPEG2000 image compression. In Proceedings of the 3rd International Conference on Introduction and Communication Technology—from Theory to Applications (ICTTA) (pp. 1–5), April 7–11, 2008. doi:10.1109/ICTTA.2008.4530087.

  • Alginahi, Y. (2010). Preprocessing techniques in character recognition. Character Recognition. In M. Mori (Ed.) (pp. 1–20). In Techopen Publishers. ISBN 978-953-307-105-3. doi:10.5772/9776.

  • Azar A. T. (2013). Fast neural network learning algorithms for medical applications. Neural Computing and Applications, 23(3–4). 1019–1034. doi:10.1007/s00521-012-1026-y.

  • Azar, A. T., & El-Said, S. A. (2013). Probabilistic neural network for breast cancer classification. Neural Computing and Applications, 23(6), 1737–1751. doi:10.1007/s00521-012-1134-8.

    Article  Google Scholar 

  • Banashree, N. P., Andhre, D., Vasanta, R., & Satyanarayana, P. S. (2007). OCR for script identification of Hindi (Devanagari) numerals using error diffusion Halftoning Algorithm with neural classifier. International Journal of Computer, Information Science and Engineering, 1(2), 281–285.

    Google Scholar 

  • Bernsen, J. (1986). Dynamic thresholding of grey-level images. In Proceedings of 8th International Conference on Pattern Recognition (pp. 1251–1255), Paris, France.

    Google Scholar 

  • Bharath, A. & Madhvanath, S. (2008). FreePad: A novel handwriting-based text input for pen and touch interfaces. In Proceedings of the 13th International Conference on Intelligent User Interfaces (pp. 297–300), New York, NY, USA. doi:10.1145/1378773.1378814.

  • Blumenstein, M., Liu, X. Y., & Verma, B. (2007). An investigation of the modified direction feature for cursive character recognition. Pattern Recognition, 40(2), 376–388. doi:10.1016/j.patcog.2006.05.017.

    Article  MATH  Google Scholar 

  • Blumenstein, M., Verma, B. & Basli, H. (2003). A novel feature extraction technique for the recognition of segmented handwritten characters. In Proceedings of the 7th International Conference on Document Analysis and Recognition (Vol. 1, pp. 137–141). Edinburgh, UK: IEEE Computer Society Press. doi:10.1109/ICDAR.2003.1227647.

  • Cavalin, P. R., Britto, A. S., Bortolozzi, F., Sabourin, R. & Oliveira, L. S. (2006). An implicit segmentation based method for recognition of handwritten strings of characters. In Proceedings of ACM Symposium on Applied Computing (SAC) (pp. 836–840), New York, NY, USA. doi:10.1145/1141277.1141468.

  • Cheng, H. D., Chen, J. R., & Li, J. (1998). Threshold selection based on fuzzy c-partition entropy approach. Pattern Recognition, 31(7), 857–870.

    Article  Google Scholar 

  • Chiang, J.-H. (1998). A hybrid neural network model in handwritten word recognition. Neural Networks, 11(2), 337–346.

    Article  Google Scholar 

  • Davies, E. (2005). Machine vision—Theory algorithms practicalities (3rd ed.). San Francisco, CA, USA: Morgan Kaufmann Publishers. ISBN 13: 978-0-12-206093-9.

    Google Scholar 

  • Desai, A. A. (2010). Gujarati handwritten numeral optical character recognition through neural network. Pattern Recognition, 43(7), 2582–2589. doi:10.1016/j.patcog.2010.01.008.

    Article  MATH  Google Scholar 

  • Farooq, F., Bhardwaj, A., Cao, H. & Govindaraju, V. (2008). Topic based language models for OCR correction. In Proceedings of the 2nd Workshop on Analytics for Noisy Unstructured Text Data (pp. 107–112), New York, NY, USA. doi:10.1145/1390749.1390766

  • Gatos, B., Pratikakis, I. & Perantonis, S. J. (2006a). Hybrid off-line cursive handwriting word recognition. In Proceedings of 18th International Conference on Pattern Recognition (ICPR’06) (Vol. 2, pp. 998–1002), Hong Kong. doi:10.1109/ICPR.2006.644.

  • Gatos, B., Pratikakis, I., Kesidis, A. L. & Perantonis, S. J. (2006b). Efficient off-line cursive handwriting word recognition. In Proceedings of the 10th International Workshop on Frontiers in Handwriting Recognition. October 2006, La Baule.

    Google Scholar 

  • Guillevic, D., Suen, C. Y. (1998). HMM-KNN word recognition engine for bank check processing. In Proceedings of International Conference on Pattern Recognition, Brisbane. Washington DC, USA: IEEE Computer Society, pp. 1526–1529. DOI: 10.1109/ICPR.1998.711998.

  • Günter, S., & Bunke, H. (2004). Feature selection algorithms for the generation of multiple classifier systems and their application to handwritten word recognition. Pattern Recognition Letters, 25(11), 1323–1336.

    Article  Google Scholar 

  • Günter, S., & Bunke, H. (2005). Off-line cursive handwriting recognition using multiple classifier systems—On the influence of vocabulary, ensemble, and training set size. Optics and Lasers in Engineering, 43(3–5), 437–454.

    Article  Google Scholar 

  • Kapur, J. N., Sahoo, P. K., & Wong, A. K. C. (1985). A New method for gray-level picture threshold using the entropy of the histogram. Computer Vision, Graphics, and Image Processing, 29, 273–285.

    Article  Google Scholar 

  • Kim, J. H., Kim, K. K., & Suen, C. Y. (2000). An HMM-MLP hybrid model for cursive script recognition. Pattern Analysis and Application, 3, 314–324.

    Article  MATH  MathSciNet  Google Scholar 

  • Koch, G, Paquet, T., Heutte, L. (2004). Combination of contextual information for handwritten word recognition. In Proceedings of 9th International Workshop on Frontiers in Handwriting Recognition, Kokubunji (pp. 468–473). doi:10.1109/IWFHR.2004.27.

  • Kundu, Y. H., & Chen, M. (2002). Alternatives to variable duration HMM in handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 1275–1280. doi:10.1109/34.730561.

    Article  Google Scholar 

  • Li, C. H., & Lee, C. K. (1993). Minimum cross entropy thresholding. Pattern Recognition, 26(4), 617–625.

    Article  Google Scholar 

  • Niblack, W. (1986). An introduction to digital image processing. Englewood Cliffs: Prentice Hall.

    Google Scholar 

  • O’Gorman, L., Sammon, M., & Seul, M. (2008). Practical algorithms for image analysis. New York, NY, USA: Cambridge University Press. ISBN 978-0 = 521-88411-2.

    MATH  Google Scholar 

  • Oliveira, L. S., Sabourin, R., Bortolozzi, F., & Suen, C. Y. (2002). Automatic recognition of handwritten numerical strings: A recognition and verification strategy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(11), 1438–1454.

    Article  Google Scholar 

  • Otsu, N. (1979). A threshold selection method from gray level histogram. IEEE Transaction on System, Man, Cybernetics, 9(1), 62–66.

    Article  MathSciNet  Google Scholar 

  • Rajashekararadhya, S. V., & Ranjan, P. V. (2009). Efficient zone based feature extraction algorithm for handwritten numeral recognition of four popular south Indian scripts. Journal of Theoretical and Applied Information Technology, 4(12), 1171–1180.

    Google Scholar 

  • Russ, J. (2007). The image processing handbook (5th ed.). Boca Raton, FL, USA: CRC Press. ISBN 0849372542.

    MATH  Google Scholar 

  • Saba, T., Sulong, G., & Rehman, A. (2011). Document image analysis: Issues, comparison of methods and remaining problems. Artificial Intelligence Review, 35(2), 101–118.

    Article  Google Scholar 

  • Sadri, J., Cheriet, M. (2009). A new approach for skew correction of documents based on particle swarm optimization. In Proceedings of 10th International Conference on Document Analysis and Recognition ICDAR ’09 (pp. 1066–1070). IEEE Computer Society, Washington, DC, USA .doi:10.1109/ICDAR.2009.268.

  • Sarfraz, M., Rasheed, Z. (2008). Skew estimation and correction of text using bounding box. In Proceedings of 5th International Conference on Computer Graphics, Imaging and Visualization (CGIV ‘08) (pp. 259–264). Washington, DC, USA: IEEE Computer Society. doi: 10.1109/CGIV.2008.10.

  • Sauvola, J., Seppänen. T., Haapakoski, S. & Pietikänen, M. (1997). Adaptive document binarization. In Fourth International Conference Document Analysis and Recognition (ICDAR) (pp. 147–152), Ulm, Germany.

    Google Scholar 

  • Shanthi, N., & Duraiswamy, K. (2009). A novel SVM-based handwritten Tamil character recognition. Pattern Analysis and Application, 13, 173–180. doi:10.1007/s10044-009-0147-0.

    Article  MathSciNet  Google Scholar 

  • Sivanandam, S. N., & Deepa, S. N. (2008). Principals of soft computing (pp. 71–83). New Delhi, India: Wiley-India. ISBN 978812652741.

    Google Scholar 

  • Tomoyuki, H., Takuma, A. & Bunpei, I. (2007). An analytic word recognition algorithm using a posteriori probability. In Proceedings of the 9th International Conference on Document Analysis and Recognition (Vol. 2, pp. 669–673), September 23–26, 2007, Tokyo. doi:10.1109/ICDAR.2007.4376999.

  • Verma, B., Blumenstein, M. (2008). Pattern recognition technologies and applications: Recent advances pp. 1–16. Hershey, New York: Information Science Reference (An Imprint of IGI Global Publications).

    Google Scholar 

  • Verma, B., Blumenstein, M., & Ghosh, M. (2004). A novel approach for structural feature extraction: Contour vs direction. Pattern Recognition Letter, 25(9), 975–988.

    Article  Google Scholar 

  • Wang, X., Ding, X., & Liu, C. (2005). Gabor filters-based feature extraction for character recognition. Pattern Recognition, 38(3), 369–379.

    Article  MATH  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Maharaja Surajmal Institute, New Delhi, India

    Amit Choudhary

  2. Maharaja Surajmal Institute of Technology, New Delhi, India

    Savita Ahlawat

  3. UIET, Maharshi Dayanand University, Rohtak, India

    Rahul Rishi

Authors
  1. Amit Choudhary
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  2. Savita Ahlawat
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  3. Rahul Rishi
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Correspondence to Amit Choudhary .

Editor information

Editors and Affiliations

  1. Department of Engineering Design and Mathematics, University of the West of England, Bristol, United Kingdom

    Quanmin Zhu

  2. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

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Choudhary, A., Ahlawat, S., Rishi, R. (2015). A Neural Approach to Cursive Handwritten Character Recognition Using Features Extracted from Binarization Technique. In: Zhu, Q., Azar, A. (eds) Complex System Modelling and Control Through Intelligent Soft Computations. Studies in Fuzziness and Soft Computing, vol 319. Springer, Cham. https://doi.org/10.1007/978-3-319-12883-2_26

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  • DOI: https://doi.org/10.1007/978-3-319-12883-2_26

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