Characters Segmentation of Cursive Handwritten Words based on Contour Analysis and Neural Network Validation

Fajri Kurniawan, Mohd. Shafry Mohd. Rahim, Ni’matus Sholihah, Akmal Rakhmadi, Dzulkifli Mohamad

Abstract


This paper presents a robust algorithm to identify the letter boundaries in images of unconstrained handwritten word . The proposed algorithm is based on  vertical  contour  analysis.  Proposed  algorithm  is  performed  to  generate  presegmentation by analyzing the vertical contours from right to left. The unwanted segmentation  points  are  reduced  using  neural  network  validation  to  improve accuracy  of  segmentation.  The  neural  network  is  utilized  to  validate segmentation  points.  The  experiments  are  performed  on  the  IAM  benchmark database.  The  results  are  showing  that  the  proposed  algorithm  capable  to accurately locating the letter boundaries for unconstrained handwritten words.


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References


Oliveira, L.S. & Sabourin, R., Support Vector Machines for Handwritten Numerical String Recognition, 9th International Workshop on Frontiers in Handwriting Recognition, Kokubunji, Tokyo, Japan, October 26-29, pp. 39-44, 2004.

Bortolozzi, F., Souza, A., Britto Jr., Oliveira, L.S. & Morita, M., Recent Advances in Handwriting Recognition, Document Analysis, Editors: Umapada Pal, Swapan K. Parui, Bidyut B. Chaudhuri, pp. 1-30, 2005.

Holt, M., Beglou, M., & Datta, S., Slant-Independent Letter Segmentation for Off-line Cursive Script Recognition, From Pixels to Features III, S. Impedovo and J.C. Simon (eds.), Elsevier, 41, 1992.

Strathy, N.W., Suen, C.Y. & Krzyyzak, A., Segmentation of Handwritten Digits using Contour Features, ICDAR ‘93, pp. 577-580, 1993.

Kimura, F., Shridhar, M. & Narasimhamurthi, N., Lexicon Directed Segmentation-Recognition Procedure for Unconstrained Handwritten Words, Pre-Proceedings IWFHR III, Buffalo, pp. 122, 1993.

Bozinovic, R.M. & Srihari, S.N., Off-line Cursive Script Word Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(1), 68-83, 1989.

Yamada, H. & Nakano, Y., Cursive Handwritten Word Recognition Using Multiple Segmentation Determined by Contour Analysis, IEICE Transactions on Information and Systems, E79-D, 464-470, 1996.

Wang, X., Zheng, K. & Guo, J., Inertial and Big Drop Fall Algorithm, International Journal of Information Technology, 12(4), 39-48, 2006.

Omidyeganeh, M., Azmi, R., Nayebi, K. & Javadtalab, A., A New Method to Improve Multi Font Farsi/Arabic Character Segmentation Results: Using Extra Classes of Some Character Combinations, SpringerVerlag Berlin Heidelberg, 670-679, 2007.

Cheng, C.K., Liu, X.Y., Blumenstein, M., & Muthukkumarasamy, V., Enhancing Neural Confidence-Based Segmentation for Cursive Handwriting Recognition, 5th International Conference on Simulated Evolution and Learning, Busan, Korea, SWA-8, 2004.

Verma, B., A Contour Code Feature Based Segmentation for Handwriting Recognition, Proceedings of 7th International Conference on Document Analysis and Recognition (ICDAR’03), pp. 1203-1207, 2003.

Heutte, L., Paquet, T., Moreau, J.V., Lecourtier, Y. & Olivier, C., A Structural/Statistical Feature based Vector for Handwritten Character Recognition, Pattern Recognition Letters, 19(7), 629- 641, 1998.

Tian, X. & Zhang, Y., Segmentation of Touching Characters in Mathematical Expressions Using Contour Feature Technique, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, pp. 206-209, 2007.

Blumenstein, M. & Verma, B., A New Segmentation Algorithm for Handwritten Word Recognition, Proceedings of the International Joint Conference on Neural Networks (IJCNN ’99), Washington D.C., pp. 878-882, 1999.

Blumenstein, M. & Verma, B., Analysis of Segmentation Performance on the CEDAR Benchmark Database, Proceedings of 6th International Conference on Document Analysis and Recognition, pp. 1142-1146, 2001.

Eastwood, B., Jennings, A. & Harvey, A., A Feature Based Neural Network Segmenter for Handwritten Words, Int’l Conf. Computational Intelligence and Multimedia Applications, Gold Coast, Australia, pp. 286-290, 1997.

Blumenstein, M., Liu, X.Y. & Verma, B., A modified direction feature for cursive character recognition, Proceedings on the IEEE International Joint Conference on Neural Networks, 4, pp. 2983-2987, 25-29 July, 2004.

Cheng, C.K. & Blumenstein, M., The Neural Based Segmentation of Cursive Words Using Enhanced Heuristics, Proceedings of the 8th International conference on Document analysis and recognition, 2, pp. 650-654, 2005.

Cheng, C.K. & Blumenstein, M., Improving the Segmentation of Cursive Handwritten Words using Ligature Detection and Neural Validation, Proceedings of the 4th Asia Pacific International Symposium on Information Technology (APIS 2005), Gold Coast, Australia, pp. 56-59, 2005.

Ghosh, M., Ghosh, R. & Verma, B., A Fully Automated Offline Handwriting Recognition System Incorporating Rule Based Neural Network Validated Segmentation and Hybrid Neural Network Classifier, International Journal of Pattern Recognition and Artificial Intelligence, 18(7), 1267-1283, 2004.

Blumenstein, M. & Verma, B., Neural-based solutions for the segmentation and recognition of difficult handwritten words from a benchmark database, Proceedings of the Fifth International Conference on 5th International Conference on Document Analysis and Recognition, Bangalore, India, 20-22 Sep, pp. 281-284, 1999.

Verma, B., A Contour Character Extraction Approach in Conjunction with A Neural Confidence Fusion Technique for the Segmentation of Handwriting Recognition, Proceedings of the 9th International Conference on Neural Information Processing (ICONIP'O2), 5, pp. 2459-2463, 2002.

Otsu, N., A threshold selection method from gray-scale histogram, IEEE Transactions on Systems, Man, and Cybernetics 9(1), 62–66, 1978.

Gader, P.D., Mohamed, M. & Chiang, J.H., Handwritten Word Recognition with Character and Inter-Character Neural Networks, IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 27, 158-164, 1997.

Marti, U, & Bunke, H., The IAM database: An English sentence database for off-line handwriting recognition, International Journal of Document Analysis and Recognition, 15, 65-90, 2002.




DOI: http://dx.doi.org/10.5614%2Fitbj.ict.2011.5.1.1

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