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Localizing and Recognizing Integral Pitches of Cheque Document Images

Authors: Bremananth R., Veerabadran C. S., Andy W. H. Khong


Automatic reading of handwritten cheque is a computationally complex process and it plays an important role in financial risk management. Machine vision and learning provide a viable solution to this problem. Research effort has mostly been focused on recognizing diverse pitches of cheques and demand drafts with an identical outline. However most of these methods employ templatematching to localize the pitches and such schemes could potentially fail when applied to different types of outline maintained by the bank. In this paper, the so-called outline problem is resolved by a cheque information tree (CIT), which generalizes the localizing method to extract active-region-of-entities. In addition, the weight based density plot (WBDP) is performed to isolate text entities and read complete pitches. Recognition is based on texture features using neural classifiers. Legal amount is subsequently recognized by both texture and perceptual features. A post-processing phase is invoked to detect the incorrect readings by Type-2 grammar using the Turing machine. The performance of the proposed system was evaluated using cheque and demand drafts of 22 different banks. The test data consists of a collection of 1540 leafs obtained from 10 different account holders from each bank. Results show that this approach can easily be deployed without significant design amendments.

Keywords: Turing Machine, Texture Analysis, Signature Verification, text localization, Cheque reading, Connectivity checking

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[1] R. Palacios, A. Gupta, and P. S. Wang, "Handwritten bank cheque recognition of courtesy amounts," Int. Journal of Image and Graphics, vol. 4, no. 2, pp. 1-20, 2004.
[2] ÔÇöÔÇö, "Feedback-based architecture for reading courtesy amounts on cheques," Journal of Electronic Imaging, vol. 12, no. 1, pp. 194-202, 2003.
[3] D. Wang, "IFCRS : An information fusion based check recognition system," in Proc. GCIS, 2009 WRI Global Congress on Intelligent Systems, vol. 2, 2009, pp. 243-247.
[4] V. K. Madasu, M. Hafizuddin, M. Yusof, M. Hanmandlu, and K. Kurt, "Automatic extraction of signatures from bank cheques and other documents," in Proc. VIIth Digital Image Computing: Techniques and Applications, 2003, pp. 591-600.
[5] V. D. Lecce, G. Dimauro, A. Guerriero, S. Impedovo, G. Pirlo, and A. Salzo, "A new hybrid approach for legal amount recognition," in Proc. 7th Int. Workshop on Frontiers in Handwriting Recognition, Amsterdam, 2000, pp. 199-208.
[6] T. C. Lee, E. J. Kim, and Y. Lee, "Error correction of Korean courtesy amounts in bank slips using rule information and cross-referencing," in Proc. Int. Conf. on Document Analysis and Recognition, 1999, pp. 95-98.
[7] N. Gorski, V. Animov, E. Augustin, O. Baret, D. Price, and J. C. Simson, "A2iA cheque reader: A family of bank recognition systems," in Proc. 5th Int. Conf. on Document Analysis and Recognition, 1999, pp. 523- 526.
[8] S. Milan, H. Vaclav, and B. Roger, Image Processing Analysis and Machine Vision. ITP, 1999.
[9] S. N. Srihari and V. Govindaraju, "Analysis of textual images using the Hough transform," Machine Vision Application, vol. 2, pp. 141-153, 1989.
[10] S. C. Hinds, J. L. Fisher, and D. P. Amato, "A document skew detection method using run-length encoding and Hough transform," in Proc. Int. Conf. on Pattern Recognition, 1990, pp. 464-468.
[11] P. Shivakumara, G. H. Kumar, D. S. Guru, and P. Nagabhushan, "A novel technique for estimation of skew in binary text document images based on linear regression analysis," Sadhana, vol. 30, no. 1, pp. 69-85, 2005.
[12] G. Martin, R. Mosfeq, and J. Pittman, "Intergrated segmented and recognition through exhaustive scans learned saccadic jumps," Int. Journal of Pattern Recognition and Artificial Intelligence, vol. 3, pp. 831-847, 1993.
[13] J. H. Kim, K. K. Kim, C. P. Nadal, and C. Y. Suen, "A methodology of combining HMM and MLP classifier for cursive word recognition," in Proc. Int. Conf. Pattern Recognition, vol. 2, Spain, 2000, pp. 319-322.
[14] Y.-K. Chen and J.-F.Wang, "Segmentation of single or multiple-touching handwritten numeral string using background and foreground analysis," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1304-1317, 2000.
[15] V. M. Nagendraprasad, P. S. P. Wang, and A. Gupta, "Algorithms for thinning and rethickening binary digital patterns," Digital Signal Processing, vol. 2, pp. 97-102, 1993.
[16] Y. Hamamoto, S. Uchimura, M. Watanabe, T. Yasuda, Y. Mitani, and S. Tomota, "A Gabor filters-based method for recognizing handwritten numberals," Pattern Recognition, vol. 31, no. 4, pp. 395-400, 1998.
[17] K. J. Anil, N. K. Ratha, and S. Lakshmanan, "Object detection using Gabor filters," Pattern Recognition, vol. 30, no. 2, pp. 295-309, 1997.
[18] K. R. Namuduri, R. Mehrotra, and N. Ranganathan, "Efficient computation of Gabor filter based multiresolution responses," Pattern Recognition, vol. 27, pp. 925-938, 1994.
[19] A. Vinciarelli, S. Bengio, and H. Bunke, "Offline recognition of unconstrained handwritten texts using HMMs and statistical language models," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 6, pp. 709-719, 2004.
[20] S. Madhvanath and V. Govindaraju, "The role of holistic paradigms in handwritten word recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 149-164, 2001.
[21] H. Baltzakis and N. Papamarkos, "A new signature verification technique based on a two-stage neural network classifier," Engineering Applications of Artificial Intelligence, vol. 14, pp. 95-103, 2001.
[22] L. Heutte, P. Barbosa-Pereira, O. Bougeois, J. V. Moreau, B. Plessis, P. Coutellemont, and Y. Lecourtier, "Multi-bank cheque recognition system: consideration on the numeral amount recognition module," Int. Journal of Pattern Recognition and Artificial Intelligence, vol. 11, no. 4, pp. 595-618, 1997.