Localizing and Recognizing Integral Pitches of Cheque Document Images
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.
[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.
[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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:51221", author = "Bremananth R. and Veerabadran C. S. and Andy W. H. Khong", title = "Localizing and Recognizing Integral Pitches of Cheque Document Images", abstract = "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 = "Cheque reading, Connectivity checking, Text localization,Texture analysis, Turing machine, Signature verification.", volume = "4", number = "1", pages = "23-11", }