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A Simple Segmentation Approach for Unconstrained Cursive Handwritten Words in Conjunction with the Neural Network.
Amjad Rehman Khan, Zulkifli Mohammad
Pages - 29 - 35     |    Revised - 06-08-2008     |    Published - 16-09-2008
Volume - 2   Issue - 3    |    Publication Date - June 2008  Table of Contents
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KEYWORDS
Image analysis, Segmentation, Neural Network, Preprocessing, Pattern matching
ABSTRACT
This paper presents a new, simple and fast approach for character segmentation of unconstrained handwritten words. The developed segmentation algorithm over-segments in some cases due to the inherent nature of the cursive words. However the over segmentation is minimum. To increase the efficiency of the algorithm an Artificial Neural Network is trained with significant amount of valid segmentation points for cursive words manually. Trained neural network extracts incorrect segmented points efficiently with high speed. For fair comparison benchmark database IAM is used. The experimental results are encouraging.
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1 Norouzi, A., Rahim, M. S. M., Altameem, A., Saba, T., Rad, A. E., Rehman, A., & Uddin, M. (2014). Medical image segmentation methods, algorithms, and applications. IETE Technical Review, 31(3), 199-213.
2 Saba, T., Rehman, A., Alkharj, K. S. A., & Al-Zahrani, S. Character Segmentation in Overlapped Script using Benchmark Database.
3 Choudhary, A. (2014). A Review of Various Character Segmentation Techniques for Cursive Handwritten Words Recognition. International Journal of Information & Computation Technology, 4(6).
4 Rehman, A., & Saba, T. (2014). Features extraction for soccer video semantic analysis: current achievements and remaining issues. Artificial Intelligence Review, 41(3), 451-461.
5 Rehman, A., & Saba, T. (2014). Neural networks for document image preprocessing: state of the art. Artificial Intelligence Review, 42(2), 253-273.
6 Saba, T., & Rehman, A. (2013). Effects of artificially intelligent tools on pattern recognition. International Journal of Machine Learning and Cybernetics, 4(2), 155-162.
7 Bangyal, W. H., Ahmad, J., & Abbas, Q. (2013). Recognition of Off-line Isolated Handwritten Character Using Counter Propagation Network. International Journal of Engineering and Technology, 5(2), 227-230.
8 Bangyal, W. H., Ahmad, J., & Abbas, Q. (2013). Analysis of Learning Rate Using CPN Algorithm for Hand Written Character Recognition Application. International Journal of Engineering and Technology, 5(2), 187.
9 Choudhary, A., Rishi, R., & Ahlawat, S. (2013). A New Approach to Detect and Extract Characters from Off-Line Printed Images and Text. Procedia Computer Science, 17, 434-440.
10 Saba, T., & Alqahtani, F. A. (2013). Semantic analysis based forms information retrieval and classification. 3D Research, 4(3), 1-6.
11 Williams, k. (2012). learning to read bushman: automatic handwriting recognition for bushman texts.
12 Sas, J., & Markowska-Kaczmar, U. (2012). Similarity-based training set acquisition for continuous handwriting recognition. Information Sciences, 191, 226-244.
13 Harouni, M., Rahim, M. S. M., Mohamad, D., Rehman, A., & Saba, T. (2012). online cursive persian/arabic character recognition by detecting critical points. International Journal of Academic Research, 4(2).
14 Elanwar, R. I., Rashwan, M., & Mashali, S. (2012). Unconstrained arabic online handwritten words segmentation using new hmm state design. In Proceedings of World Academy of Science, Engineering and Technology (No. 64). World Academy of Science, Engineering and Technology.
15 Rehman, A. (2012). Machine learning based air traffic control strategy. International Journal of Machine Learning and Cybernetics, 1-11.
16 Rehman, A., & Saba, T. (2012). Off-line cursive script recognition: current advances, comparisons and remaining problems. Artificial Intelligence Review, 37(4), 261-288.
17 Saba, T., Alzorani, S., & Rehman, A. (2012). Expert system for offline clinical guidelines and treatment. Life Science Journal, 9(4), 2639-2658.
18 Saba, T. (2012). Implications of E-learning systems and self-efficiency on students outcomes: a model approach. Human-Centric Computing and Information Sciences, 2(1), 1-11.
19 Norouzi, A., Saba, T., Rahim, M., Shafry, M., Amin, I. M., & Rehman, A. (2012). visualization and segmentation of 3d bone from ct images. International Journal of academic research, 4(2).
20 G. Sulong , A. Rehman and T. Saba."Improved Offline Connected Script Recognition Based on Hybrid Strategy".International Journal of Engineering Science and Technology, 2(6):1603-1611, 2010
21 Mohanty, S., & Bebartta, H. N. D. (2010). A Novel Approach for Bilingual (English-Oriya) Script Identification and Recognition in a Printed Document. International Journal of Image Processing (IJIP), 4(2), 175.
22 Arora, S., Bhattacharjee, D., Nasipuri, M., Basu, D. K., & Kundu, M. (2010). Recognition of non-compound handwritten devnagari characters using a combination of mlp and minimum edit distance. arXiv preprint arXiv:1006.5908.
23 Saba, T., Sulong, G., & Rehman, A. (2010). A survey on methods and strategies on touched characters segmentation. International Journal of Research and Reviews in Computer Science, 1(2), 103-114.
24 Angadi, S. A., & Kodabagi, M. M. (2009). A texture based methodology for text region extraction from low resolution natural scene images. International Journal of Image Processing, 3(5), 229-245.
25 Rehman, A., Mohamad, D., & Sulong, G. (2009). Implicit vs explicit based script segmentation and recognition: a performance comparison on benchmark database. Int. J. Open Problems Compt. Math, 2(3), 352-364.
26 Rehman, A., Mohamad, D., Kurniawan, F., & Ilays, M. (2009, May). Performance analysis of segmentation approach for cursive handwriting on benchmark database. In Computer Systems and Applications, 2009. AICCSA 2009. IEEE/ACS International Conference on (pp. 265-270). IEEE.
1 Google Scholar 
2 ScientificCommons 
3 Academic Index 
4 CiteSeerX 
5 refSeek 
6 iSEEK 
7 Socol@r  
8 ResearchGATE 
9 Bielefeld Academic Search Engine (BASE) 
10 Scribd 
11 WorldCat 
12 SlideShare 
13 PDFCAST 
14 PdfSR 
1 H.Bunke. “Recognition of cursive roman handwriting, past, present and future”. In Proceeding of 7th International Conference on Document Analysis and Recognition, 448- 461, 2003.
2 C.Y.Suen, R.Legault, C. Nadal, M.Cheriet and L.Lam. “Building a new generation of handwriting recognition systems”. Pattern Recognition Letters, 14: 305-315, 1993.
3 S.W. Lee. “Multilayer cluster neural network for totally unconstrained handwritten numerical recognition”. Neural Networks, 8: 783-792, 1995
4 H. I. Avi-Itzhak, T. A. Diep and H.Garland. “High accuracy optical character recognition using neural networks”. IEEE Trans. Pattern Analysis and Machine Intelligence, 18: 648- 652, 1996.
5 S.W. Lee. “Off-line recognition of totally unconstrained handwritten numericals using multilayer cluster neural network”. IEEE Trans Pattern Analysis and Machine Intelligence, 18: 648-652, 1996.
6 S.B. Cho. “Neural networks classifiers for recognition totally unconstrained handwritten numericals”. IEEE Trans. On Neural Networks, 8.
7 R. Farag. “Word–level recognition of cursive script”. IEEE Trans. Computing 28: 172-175, 1979.
8 R.G. Casey, E. Lecolinet. “A survey of methods and strategies in character segmentation”. IEEE Trans. Pattern Analysis and Machine Intelligence, 18: 690-706, 1996.
9 J. Wang, J. Jean. “Segmentation of merged characters by neural networks and shortest path” Pattern Recognition 27(5): 649-658, 1994.
10 J. M. Bertille, M. E. Yacoubi. “Global cursive postal code recognition using hidden Marko models”. In Proceeding of the First European Conference Postal Technology, France, 129-138, 1993.
11 S.N. Srihari. “Recognition of handwritten and machine printed text for postal address interpretation”. Pattern recognition letters, 14: 291-302, 1993.
12 C. K. Cheng, M. Blumenstein. "The neural-based segmentation of cursive words using enhanced heuristics". In Proceedings of Eighth International Conference on Document Analysis and Recognition (ICDAR'05), 650-654, 2005.
13 M. Blumenstein, B. K. Verma. “An artificial neural network based segmentation algorithm for off-line handwriting recognition”. In proceedings of International Conference on Computational Intelligence and Multimedia Applications (ICCIMA’ 98), Gippsland, Australia, 1997.
14 M. Gilloux. “Research into the new generation of character and mailing address recognition systems at the French post office research center”. Pattern Recognition Letters.14: 267-276 1993.
15 X. Xiao, G. Leedham. “Knowledge–based English cursive script segmentation”. Pattern recognition letters 21: 945-954, 2000.
16 M. Giloux. “Hidden Markov Models in Handwriting Recognition”. Fundamentals in Handwriting Recognition, S.Impedovo ed., NATO ASI Series F: Computer and System Science, 24: Spinger Verlang, 1994.
17 M. EI. Yacoub, M. Gilloux and J. M. Bertille. “A statistical approach for phrase location and recognition with in a text Line”. An application to Street Name Recognition, IEEE Trans. Pattern Analysis and Machine Intelligence, 24(2): 172-188, 2002.
18 A. Khotanzad, J. Lu. “Shape and texture recognition by a neural network”. Artificial Neural Networks in Pattern Recognition, Elsevier Science Publishers B.V., Amsterdam, Netherlands, 109-131, 1991.
19 20. A.D. Kulkarni. “Artifical neural networks for image understanding”. Van Nostrand Reinhold, New York. 154-270, 1994.
20 B. Zheng, W. Qian and L.Clarke. “Multistage neural network for pattern recognition in mammogram screening”. IEEE ICNN, Orlando, 3437-3448, 1994.
21 N. Otsu. “A threshold selection method from gray level histograms”. IEEE transactions on systems, Man and Cybernetics, 9(1): 62-66, 1979.
22 K. Han, I. K. Sethi. “Handwriting signature retrieval and Identification”. Pattern Recognition Letters, 17, 83-90, 1996.
23 S. Knerr, E. Augustin. “A neural network–hidden markov model hybrid for cursive word recognition”. In Proceedings of International Conference on Pattern Recognition, Brisbane, 2: 1518-1520, 1998.
24 U. Marti, H. Bunke. “The IAM database: An English sentence database for off-line handwriting recognition”. International Journal of Document Analysis and Recognition, 15: 65-90, 2002.
25 C. C. Tappert., C. Y. Suen and T. Wakahara. “The state of the art in on-line handwriting recognition”. IEEE Trans. Pattern Analysis. Machine. Intelligence. 12: 787-808, (1990)
26 S. N. Srihari. “Recognition of handwritten and machine-printed text for postal address Interpretation”. Pattern Recognition Letters 291-302, 1993.
27 S-W. Lee, D-J. Lee and H-S Park. “A new methodology for gray–scale character segmentation and recognition”. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1045-1051, 1996.
28 K. Han, I. K. Sethi. “Off-line cursive handwriting segmentation”. ICDAR 95, Montreal, Canada, 894-897, 1995
29 B. Eastwood, A. Jennings and A. Harvey. “A feature based neural network segmenter for handwritten words”. ICCIMA’97 Australia, 286-290. 1997
30 M. Blumenstein, B. Verma. “A segmentation algorithm used in conjunction with artificial neural networks for the recognition of real-word postal addresses”. In Proceeding of International Conference on Computational Intelligence and Multimedia Applications (ICCIMA’97), Gold Coast, Australia. 155-160, 1997.
31 B. Yanikoglu, P.A.Sandon. “Segmentation of off-line cursive handwriting using linear programming”. Pattern Recognition, 31: 1825-1833, 1998.
32 G. Nicchiotti, C.Scagliola. “Generalized projections: a tool for cursive handwriting normalisation”. In Proceedings of 5th International Conference on Document Analysis and Recognition, Bangalore, 729-733, 1999.
33 B. Verma, P. Gader. "Fusion of multiple handwritten word recognition techniques". Neural Networks for Signal Processing X, 2000. In Proceedings of the IEEE Signal Processing Society Workshop, 2: 926-934, 2000.
34 M. Blumenstein, B. Verma. "Analysis of segmentation performance on the CEDAR benchmark database". In Proceedings of Sixth International Conference on Document Analysis and Recognition (ICDAR'01), 1142, 2001.
35 C. K. Cheng., X. Y. Liu., M. Blumenstein and V. Muthukkumarasamy. “Enhancing neural confidence-based segmentation for cursive handwriting recognition”. In Proceeding of 5th International Conference on Simulated Evolution and Learning (SEAL '04), Busan, Korea, SWA-8, 2004.
36 B. Verma. "A contour character extraction approach in conjunction with a neural confidence fusion technique for the segmentation of handwriting recognition”. In Proceedings of the 9th International Conference on Neural Information Processing. 5: 18- 22, 2002.
37 C. K. Cheng, M. Blumenstein. “Improving the segmentation of cursive handwritten words using ligature detection and neural validation”. In Proceedings of the 4th Asia Pacific International Symposium on Information Technology (APIS 2005), Gold Coast, Australia, 56-59, 2005.
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Mr. Amjad Rehman Khan
Department of Computer Graphics and Multimedia - Malaysia
amjadbzu2003@yahoo.com
Dr. Zulkifli Mohammad
Department of Computer Graphics and Multimedia - Malaysia