Skip to main content
SpringerLink
Log in

Automatic processing of handwritten bank cheque images: a survey

  • Original Paper
  • Published:
International Journal on Document Analysis and Recognition (IJDAR) Aims and scope Submit manuscript

Abstract

Bank cheques (checks) are still widely used all over the world for financial transactions. Huge volumes of handwritten bank cheques are processed manually every day in developing countries. In such a manual verification, user written information including date, signature, legal and courtesy amounts present on each cheque has to be visually verified. As many countries use cheque truncation systems (CTS) nowadays, much time, effort and money can be saved if this entire process of recognition, verification and data entry is done automatically using images of cheques. An attempt is made in this paper to present the state of the art in automatic processing of handwritten cheque images. It discusses the important results reported so far in preprocessing, extraction, recognition and verification of handwritten fields on bank cheques and highlights the positive directions of research till date. The paper has a comprehensive bibliography of many references as a support for researchers working in the field of automatic bank cheque processing. The paper also contains some information about the products available in the market for automatic cheque processing. To the best of our knowledge, there is no survey in the area of automatic cheque processing, and there is a need of such a survey to know the state of the art.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Abbreviations

ANN:

Artificial neural network

BC:

Bayesian classifier

BN:

Bayesian network

BPNN:

Back-propagation neural networks

CM:

Co-occurrence matrix

CTS:

Cheque truncation system

DBC:

Differential box counting

DFA:

Deterministic finite automation

DTW:

Dynamic time warping

ED:

Euclidean distance

EDF:

Extended drop fall

EER:

Equal error rate

FAR:

False acceptance rate

FFNN:

Feed-forward neural network

FKNN:

Fuzzy K-nearest neighbour

FNN:

Fuzzy neural network

FPS:

Fixed point-spread

FRR:

False rejection rate

GB:

Global baseline

GLS:

Grey-level space

GRNN:

Generalized regression neural network

GSC:

Gradient, structural and concavity

HDF:

Hybrid drop fall

HDS:

Hit and deflect strategy

HMM:

Hidden Markov models

HMRF:

Hidden Markov random field

HNN:

Hopfield neural nets

HNNC:

Hierarchical neural network classifier

HT:

Hough transform

ICS:

Image-based clearing system

IQA:

Image quality assurance

IRD:

Image replacement document

KNN:

K-nearest neighbour

LBP:

Local binary pattern

LGSD:

Local granulometric size distributions

LS-SVM:

Least squares support vector machines

MBR:

Minimum bounding rectangle

MD:

Mahalanobis distance

MDC:

Minimum distance classifier

ME:

Multi expert

MICR:

Magnetic ink character recognition

ML:

Maximum likelihood

MLP:

multi-layer perceptron

MQDF:

Modified quadratic discriminant function

MM:

Mathematical morphology

MMI:

Maximum mutual information

MPR:

Most probable region

MRS:

Multi resolution shape

MSFC:

Multiple structural feature classifier

MSI:

Model Sub-Image

MVBC:

Majority vote method based on Borda count function

NN:

Neural network

NNC:

Nearest neighbour classifier

OCR:

Optical character recognition

OGMM:

Orthogonal Gaussian mixture model

PCAC:

Principal component analysis classifier

PCC:

Pseudo-cepstral coefficients

PF:

Pressure features

PGM:

Probabilistic graphical model

PNV:

Payee Name Verification

RBF:

Radial basis function

RBFNN:

Radial basis function neural network

ROC:

Receiver operating characteristic

RPBF:

Reference pattern based features

RS:

Random subspaces

SB:

Structural-based

SC:

Symbolic classifier

SDT:

Syntax directed translation

SF:

Slant features

SLFFNN:

Simple-layer feed-forward neural network

SOM:

Self-organizing map

SSE:

Sum-of-squared error

SVM:

Support vector machines

TB:

Template based

TS:

Takagi–Sugeno

TDNN:

Time delay neural network

TSI:

Target sub image

References

  1. Palacios R., Gupta A.: A system for processing handwritten bank checks automatically. Image Vis. Comput. 26(10), 1297–1313 (2008)

    Article  Google Scholar 

  2. Han K., Sethi I.K.: An off-line cursive handwritten word recognition system and its application to legal amount interpretation. Int. J. Pattern Recognit. Artif. Intell. 11(5), 757–770 (1997)

    Article  Google Scholar 

  3. Zhang, H.G., Chen, G., Liu, G., Guo, J.: Bank check image binarization based on signal matching. In: Proceedings of the Fifth International Conference on Information, Communications and Signal Processing, pp. 1430–1433 (2005)

  4. Zanchettin, C., Cavalcanti, G.D.C., Doria, R.C., Silva, E.F.A., Rabelo, J.C.B., Bezerra, B.L.D.: A neural architecture to identify courtesy amount delimiters. In: Proceedings of the International Joint Conference on Neural Networks, pp. 3210–3217 (2006)

  5. http://www.banknetindia.com/banking/chqtruncationb.htm

  6. http://www.rosettatechnologies.com/image-replacement-document.asp

  7. Leroux M., Lethelier E., Gilloux M., Lemarie B.: Automatic reading of handwritten amounts on French checks. Int. J. Pattern Recognit. Artif. Intell. 11(4), 619–638 (1997)

    Article  Google Scholar 

  8. Gorski N.: Bank cheque data mining: integrated cheque recognition technologies. In: Chaudhuri, B.B. (eds) Digital Document Processing, pp. 437–458. Springer, Berlin (2007)

    Chapter  Google Scholar 

  9. Impedovo, S., Wang, P., Bunke, H. (eds): Automatic Bank Check Processing. World Scientific, Singapore (1997)

    Google Scholar 

  10. http://www.3i-infotech.com/content/payment_solutions/image_quality_assurance.aspx

  11. www.a2ia.com/Addon_Site/Upload/Autres/checkreader_3.4.pdf

  12. http://www.sbullet.com/products_IQA_benefits.html

  13. Lin, X.,: Quality assurance in high volume document digitization: a survey. In: Proceedings of 2nd International Conference on Document Image Analysis for Libraries, pp. 312–319 (2006)

  14. Ulges, A., Lampert, C.H., Breuel, T.M.: Document image dewarping using robust estimation of curled text lines. In: Proceedings of 8th International Conference on Document Analysis and Recognition, pp. 1001–1005 (2005)

  15. Zhang, L., Tan, C.L.: Warped image restoration with applications to digital libraries. In: Proceedings of 8th International Conference on Document Analysis and Recognition, pp. 192–196 (2005)

  16. Cao, H., Ding, X., Liu, C.: Rectifying the bound document image captured by the camera: a model based approach. In: Proceedings 7th International Conference on Document Analysis and Recognition, pp. 71–75 (2003)

  17. Fan, J., Lin, X., Simske, S.: A comprehensive image processing suite for book re-mastering. In: Proceedings of 8th International Conference on Document Analysis and Recognition, pp. 447–451 (2005)

  18. Le Bourgeois, F., Trinh, E. et al.: Document image analysis solutions for digital libraries. In: Proceedings International Workshop on Document Image Analysis for Libraries, pp. 2–24 (2004)

  19. Liu K., Suen C.Y., Cheriet M., Said J.N., Nadal C., Tang Y.Y.: Automatic extraction of baselines and data from check images. Int. J. Pattern Recognit. Artif. Intell. 11(4), 675–697 (1997)

    Article  Google Scholar 

  20. Boutros, G.: Automating Degraded Image Enhancement Processing (DIEP), Symposium on Document Image Understanding Technology, College Park, Maryland (2005)

  21. Smith, E.H.B., Baird, H., Barrett, W., Bourgeois, F.L., Lin, X., Nagy, G., Simske, S.: DIAL 2004 working group report on acquisition quality control. In: Proceedings of the Second International Conference on Document Image Analysis for Libraries, pp. 373–376 (2006)

  22. Garain, U., Halder, B.: Machine authentication of security documents. In: Proceedings of 10th International Conference on Document Analysis and Recognition, pp. 718–722 (2009)

  23. http://www.carreker.fiserv.com/main/solutions/payments/CO/pnv.htm

  24. Dawoud A., Kamel M.: Iterative model-based binarization algorithm for cheque images. Int. J. Document Anal. Recognit. 5, 28–38 (2002)

    Article  MATH  Google Scholar 

  25. Sahoo P.K., Soltani S., Wong A.K.C., Chen Y.C.: A survey of thresholding techniques. Comput. Vis. Graph. Image Process. 41(2), 233–260 (1988)

    Article  Google Scholar 

  26. Otsu N.: A threshold selection method from gray-scale histogram. IEEE Trans. Syst. Man Cybern. 8, 62–66 (1978)

    Article  Google Scholar 

  27. Trier O.D., Jain A.K.: Goal-directed evaluation of binarization methods. IEEE Trans. Pattern Anal. Mach. Intell. 17(12), 1191–1201 (1995)

    Article  Google Scholar 

  28. Niblack W.: An Introduction to Digital Image Processing. Prentice-Hall, Englewood Cliffs (1986)

    Google Scholar 

  29. Yanowitz S.D., Bruckstein A.M.: A new method for image segmentation. Comput. Vis. Graph. Image Process. 46, 82–95 (1989)

    Article  Google Scholar 

  30. White J.M., Rohrer G.D.: Image thresholding for optical character recognition and other applications requiring character image extraction. IBM J. Res. Dev. 27(4), 400–411 (1983)

    Article  Google Scholar 

  31. Trier O.D., Taxt T.: Improvement of integrated function algorithm for binarization of document images. Pattern Recognit. Lett. 16, 277–283 (1995)

    Article  Google Scholar 

  32. Parker J.R.: Gray level thresholding in badly illuminated images. IEEE Trans. Pattern Anal. Mach. Intell. 13, 813–819 (1991)

    Article  Google Scholar 

  33. Sezgin M., Sankur B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 317–327 (2004)

    Article  Google Scholar 

  34. Mello, C.A.B., Bezerra, B.L.D., Zanchettin, C., Macario, V.: An efficient thresholding algorithm for Brazilian bank checks. In: Proceedings of 9th ICDAR, pp. 193–197 (2007)

  35. Suen C.Y., Lam L., Guillevic D., Strathy N.W., Cheriet M., Said J.N., Fan R.: Bank check processing system. Int. J. Imaging Syst. Technol. 7, 392–403 (1996)

    Article  Google Scholar 

  36. Madasu, V.K., Lovell, B.C.: Automatic segmentation and recognition of bank cheque fields. In: Proceedings of the Digital Imaging Computing: Techniques and Applications, pp. 33–38 (2005)

  37. Neves, R.F.P., Mello, C.A.B., Silva, M.S., Bezerra, B.L.D. A new algorithm to threshold the courtesy amount of Brazilian bank checks. In: Proceedings of IEEE International Conference on Systems Man and Cybernetics, pp. 1226–1230 (2008)

  38. Lee L.L., Lizarraga M.G., Gomes N.R., Koerich A.L.: A prototype for Brazilian bank check recognition. Int. J. Pattern Recognit. Artif. Intell. 11(4), 549–570 (1997)

    Article  Google Scholar 

  39. Chandra, L., Gupta, R., Kumar, P., Ganotra, D.: Automatic courtesy amount recognition for Indian bank checks. In: Proceedings of IEEE Region 10 Conference, 2008, pp. 1–5 (2008)

  40. Su H., Zhao B., Ma F., Wang S., Xia S.: A fault tolerant Chinese check recognition system. Int. J. Pattern Recognit. Artif. Intell. 11(4), 571–594 (1997)

    Article  Google Scholar 

  41. Hull J.J.: Document image skew detection: survey and annotated bibliography. In: Hull, J.J., Taylor, S.L. (eds) Document Analysis Systems II, pp. 40–64. World Scientific, Singapore (1998)

    Chapter  Google Scholar 

  42. Knerr S., Anisimov V., Baret O., Gorski N., Price D., Simon J.C.: The A2iA intercheque system: courtesy amount and legal amount recognition for French checks. Int. J. Pattern Recognit. Artif. Intell. 11(4), 505–548 (1997)

    Article  Google Scholar 

  43. Akiyama A., Hagita N.: Automated entry system for printed documents. Pattern Recognit. 23, 1141–1154 (1990)

    Article  Google Scholar 

  44. Vinciarelli A.: A survey on off-line cursive word recognition. Pattern Recognit. 35(7), 1433–1446 (2002)

    Article  MATH  Google Scholar 

  45. Steinherz T., Rivlin E., Intrator N.: Offline cursive script word recognition—a survey. Int. J. Document Anal. Recognit. 2(2), 90–110 (1999)

    Article  Google Scholar 

  46. Kim G., Govindaraju V.: Bank check recognition using cross validation between legal and courtesy amounts. Int. J. Pattern Recognit. Artif. Intell. 11(4), 657–674 (1997)

    Article  Google Scholar 

  47. Guillevic D., Suen C.Y.: Recognition of legal amounts on bank cheques. Pattern Anal. Appl. 1(1), 28–41 (1998)

    Article  Google Scholar 

  48. Guillevic, D., Suen, C.Y.: Cursive script recognition applied to the processing of bank cheques. In: Proceedings of 3rd ICDAR, pp. 11–14 (1995)

  49. Guillevic, D.: Unconstrained Handwriting Recognition Applied to the Recognition of Bank Cheques. PhD thesis, Concordia University (1995)

  50. Kaufmann G., Bunke H.: Automated reading of cheque amounts. Pattern Anal. Appl. 3, 132–141 (2000)

    Article  Google Scholar 

  51. Kimura F., Tsuruoka S., Miyake Y., Shridhar M.: A lexicon directed algorithm for recognition of unconstrained handwritten words. IEICE Trans. Inf. Syst E77-D(7), 785–793 (1994)

    Google Scholar 

  52. Srikantan, G., Lee, D.S., Favata, J.T.: Comparison of normalization methods for character recognition. In: Proceedings of 3rd ICDAR, pp.719–722 (1995)

  53. Dzuba G., Filatov A., Gershuny D., Kil I., Nikitin V.: Check amount recognition based on the cross validation of courtesy and legal amount fields. Int. J. Pattern Recognit. Artif. Intell. 11(4), 639–655 (1997)

    Article  Google Scholar 

  54. Liu, K., Suen, C.Y., Nadal, C.: Automatic extraction of items from cheque images for payment recognition. In: Proceedings of 13th ICPR, pp. 798–802 (1996)

  55. Okada M., Shridhar M.: Extraction of user entered components from a personal bank check using morphological subtraction. Int. J. Pattern Recognit. Artif. Intell. 11(5), 699–715 (1997)

    Article  Google Scholar 

  56. Heutte L., Pereira P.B., Bougeois O., Moreau J.V., Plesis B., Courtellemont P., Lecourtier Y.: Multi-bank check recognition system: consideration on the numeral amount recognition module. Int. J. Pattern Recognit. Artif. Intell. 11(4), 595–618 (1997)

    Article  Google Scholar 

  57. Khan, M.R.H., Hossain, G.: Bengali handwritten bank check recognition using automatic extraction of the User–Entered Data. In: Proceedings of 8th ICCIT, pp. 650–654 (2005)

  58. Koerich, A.L., Lee, L.L.: A novel approach for automatic extraction of the user entered data from bankchecks. In: Proceedings of International Workshop on Document Analysis Systems, pp. 141–144 (1998)

  59. Agarwal, A., Granowetter, L., Hussein, K., Gupta, A., Wang, P.S.P.: Detection of courtesy amount block on bank checks. In: Proceedings of 3rd ICDAR, pp. 748–751 (1995)

  60. Samoud, F.B., Maddouri, S.S., Abed, H.E., Ellouze, N.: Comparison of two handwritten Arabic zones extraction methods of complex documents. In: Proceedings of International Arab Conference on Information Technology, pp. 1–7 (2008)

  61. Ueda, K., Maegawa, H., Matsuo, K.: Automatic extraction of filled-in items from bank-check images. In: Proceedings of International Workshop on Document Analysis Systems, pp. 225–228 (2004)

  62. Huang, L., Li, S., Li, L.: Extraction of filled-in items from Chinese bank check using support vector machines. In: Proceedings of 4th International Symposium on Neural Networks, pp. 407–415 (2007)

  63. Djeziri, S., Nouboud, F., Plamondon, R.: Extraction of items from checks. In: Proceedings of 4th ICDAR, pp. 749–752 (1997)

  64. Alirezaee, S., Ahmadi, M., Alijamat, A., Alahiari, M.A.: Segmentation of Iranian bank checks. In: Proceedings of the 12th IEEE International Multitopic Conference, pp. 460–462 (2008)

  65. Ye X., Cheriet M., Suen C.Y., Liu K.: Extraction of bank check items by mathematical morphology. Int. J. Document Anal. Recognit. 2, 53–66 (1999)

    Article  Google Scholar 

  66. Xu, W., Zhang, H., Guo, J., Chen, G.: Discrimination between printed and handwritten characters for cheque OCR system. In: Proceedings of International Conference on Machine Learning and Cybernetics, pp. 1048–1053 (2002)

  67. Suen C.Y., Xu Q., Lam L.: Automatic recognition of handwritten data on cheques—fact or fiction?. Pattern Recognit. Lett. 20, 1287–1295 (1999)

    Article  Google Scholar 

  68. Fan, R., Lam, L., Suen, C.Y.: Processing of date information on cheques. In: Proceedings of the Fifth International Workshop on Frontiers in Handwriting Recognition, pp. 207–212 (1996)

  69. Lam, L., Fan, R., Xu, Q., Suen, C.Y.: Segmentation of date field on bank cheques. In: Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition, pp. 163–172 (2000)

  70. Xu, Q., Lam, L., Suen, C.Y.: Automatic segmentation and recognition system for handwritten dates on Canadian bank cheques. In: Proceedings of the Seventh International Conference on Document Analysis and Recognition, pp. 704–708 (2003)

  71. Santos, J.E.B.D., Dubuisson, B., Bortolozzi, F.: Characterizing and distinguishing text in bank cheque images. In: Proceedings of the 15th Brazilian Symposium on Computer Graphics and Image Processing, pp. 203–209 (2002)

  72. Guo, J.K., Ma, M.Y.: Separating handwritten material from machine printed text using hidden Markov models. In: Proceedings of the Sixth International Conference on Document Analysis and Recognition, pp. 439–443 (2001)

  73. Ma, M., Guo, K.: Detecting and Utilizing Add-on Information from a Scanned Document Image. Technical Report PINTL-IM-142-099. Panasonic Information and Networking Technologies Laboratory (2000)

  74. Madasu, V.K., Yusof, M.H.M., Hanmandlu, M., Kubik, K.: Automatic extraction of signatures from bank cheques and other documents. In: Proceedings of DICTA, pp. 591–600 (2003)

  75. Djeziri S., Nouboud F., Plamondon R.: Extraction of signatures from check background based on a filiformity criterion. IEEE Trans. Image Process. 7(10), 1425–1438 (1998)

    Article  Google Scholar 

  76. Chaudhuri B.B., Sarkar N.: Texture segmentation using fractal dimension. IEEE Trans. Pattern Anal. Mach. Intell. 17(1), 72–77 (1995)

    Article  Google Scholar 

  77. Casey R.G., Lecolinet E.: A survey of methods and strategies in character segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 18((7), 690–706 (1996)

    Article  Google Scholar 

  78. Trier D., Jain A.K., Taxt T.: Feature extraction methods for character recognition—a survey. Pattern Recognit. 29(4), 641–662 (1996)

    Article  Google Scholar 

  79. Govindan V.K., Shivaprasad A.P.: Character recognition: a review. Pattern Recognit. 23(7), 671–683 (1990)

    Article  Google Scholar 

  80. Fenrich R.: Segmenting of automatically located handwritten numeral strings. In: Impedovo, S., Simon, J.C. (eds) From Pixels to Features III-Frontiers in Handwriting Recognition, pp. 47–60. Elsevier, Amsterdam (1992)

    Google Scholar 

  81. Zhang, L.Q., Suen, C.Y.: Recognition of courtesy amounts on bank checks based on a segmentation approach. In: Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition, pp. 298–302 (2002)

  82. Westall J.M., Narasimhsa M.S.: An evolutionary approach to the use of neural networks in the segmentation of handwritten numerals. Int. J. Pattern Recognit. Artif. Intell. 11(5), 717–735 (1997)

    Article  Google Scholar 

  83. Dimauro G., Impedovo S., Salzo A.: Automatic bankcheck processing: a new engineered system. Int. J. Pattern Recognit. Artif. Intell. 11(4), 467–504 (1997)

    Article  Google Scholar 

  84. Dimauro, G., Impedovo, S., Pirlo, G., Salzo, A.: Bankchecks recognition systems: reengineering the design process. In: Downton, A.C., Impedovo, S. (eds.) Progress in handwriting recognition, pp. 419–425. World Scientific Publishing (1997)

  85. Lam, L., Suen, C.Y., Guillevic, D., Strathy, N.W., Cheriett, M., Liu, K., Said, J.N.: Automatic processing of information on cheques. In: Proceedings of ICSMC, pp. 2353–2358 (1995)

  86. Dey, S.: Adding Feedback to Improve Segmentation and Recognition of Handwritten Numerals. Master Thesis, Massachusetts Institute of Technology (1999)

  87. Khan, S.: Character Segmentation Heuristics of Check Amount Verification. Master Thesis, Massachusetts Institute of Technology (1998)

  88. Punnoose, J.: An Improved Segmentation Module for Identification of Handwritten Numerals. Master Thesis, Massachusetts Institute of Technology (1999)

  89. Chuang Z., Zhiqing L., Bo X., Jun G.: The segmentation algorithm for handwritten numeral strings in bank-check recognition. Front. Electr. Electron. Eng. China 2(1), 39–44 (2007)

    Article  Google Scholar 

  90. Hussein K., Agarwal A., Gupta A., Wang P.: A knowledge based segmentation algorithm for enhanced recognition of handwritten courtesy amounts. Pattern Recognit. 32(2), 305–316 (1999)

    Article  Google Scholar 

  91. Oliveira L.S., Sabourin R., Bortolozzi F., Suen C.Y.: Automatic recognition of handwritten numerical strings: a recognition and verification strategy. IEEE Trans. Pattern Anal. Mach. Intell. 24(11), 1438–1454 (2002)

    Article  Google Scholar 

  92. http://en.wikipedia.org/wiki/Artificial_neural_network

  93. Palacios R., Gupta A., Wang P.S.P.: Feedback-based architecture for reading courtesy amounts on checks. J. Electron. Imaging 12(1), 194–202 (2003)

    Article  Google Scholar 

  94. Palacios, R., Gupta, A.: Training neural networks for reading handwritten amounts on checks. In: Proceedings of IEEE International Workshop on Neural Networks for Signal Processing, pp. 607–616 (2003)

  95. Palacios R., Gupta A., Wang P.S.P.: Handwritten bank check recognition of courtesy amounts. Int. J. Image Graph. 4(2), 1–20 (2004)

    Article  Google Scholar 

  96. Filatov, A., Gitis, A., Kil, I.: Graph based handwritten digit string recognition. In: Proceedings of ICDAR, pp. 845–848 (1995)

  97. Song, W., Feng, M., Shaowei, X., Hui, S.: A fault tolerant Chinese bank check recognition system based on SOM neural networks. In: Proceedings of International Conference on Neural Networks, pp. 2560–2565 (1997)

  98. Gorski N., Anisimov V., Augustin E., Baret O., Maximov S.: Industrial bank check processing: the A2iA check-reader. Int. J. Document Anal. Recognit. 3, 196–206 (2001)

    Article  Google Scholar 

  99. Gorski, N., Anisimov, V., Augustin, E., Baret, O., Price, D., Simon, J.C.: A2iA Check Reader: a family of bank check recognition systems. In: Proceedings of 5th ICDAR, pp. 523–526 (1999)

  100. Gorski N.: Practical combination of multiple classifiers. In: Downton, A., Impedovo, S. (eds) Progress in Handwriting Recognition, World Scientific, Singapore (1997)

    Google Scholar 

  101. Jayadevan, R., Pal, U., Kimura, F., Recognition of Words from legal amounts of Indian bank cheques. In: Proceedings of 12th ICFHR, pp. 166–171 (2010)

  102. Madhvanath S., Govindaraju V.: The role of holistic paradigms in handwritten word recognition. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 149–164 (2001)

    Article  Google Scholar 

  103. Lu Y., Shridhar M.: Character segmentation in handwritten words—an overview. Pattern Recognit. 29(1), 77–96 (1996)

    Article  Google Scholar 

  104. Govindaraju V., Srihari S.H.: Separating handwritten text from interfering strokes. In: Impedovo, S., Simon, J.C. (eds) From Pixels to Features III-Frontiers in Handwriting Recognition, pp. 17–28. Elsevier, Amsterdam (1992)

    Google Scholar 

  105. Dimauro, G., Impedovo, S., Pirlo, G., Salzo, A.: Removing underlines from handwritten text: an experimental investigation. In: Downton, A.C., Impedovo, S. (eds.) Progress in Handwriting Recognition, pp. 497–501. World Scientific Publishing (1997)

  106. Yu M.L., Kwok P.C.K., Leung C.H., Tse K.W.: Segmentation and recognition of Chinese bank check amounts. Int. J. Document Anal. Recognit. 3, 207–217 (2001)

    Article  Google Scholar 

  107. Yu, C.L., Suen, C.Y., Tang, Y.Y. Location and recognition of legal amounts on Chinese bank cheques. In: Proceedings of 4th ICDAR, pp. 588–591 (1997)

  108. Dimauro, G., Impedovo, S., Pirlo, G.: Automatic recognition of cursive amounts on Italian bank-checks. In: Impedovo, S. (ed.) Progress in Image Analysis and Processing III, pp. 323–330. World Scientific (1994)

  109. http://en.wikipedia.org/wiki/Hidden_Markov_model

  110. Guillevic, D., Suen, C.Y.: HMM word recognition engine. In: Proceedings of the 4th International Conference on Document Analysis and Recognition, pp. 544–547 (1997)

  111. Knerr S., Augustin E., Baret O., Price D.: Hidden Markov model based word recognition and its application to legal amount reading on French checks. Comput. Vis. Image Underst. 70(3), 404–419 (1998)

    Article  Google Scholar 

  112. Augustin, E., Baret, O., Price, D., Knerr, S.: Legal amount recognition on French bank checks using a neural network-HMM hybrid. In: Proceedings of IWFHR, pp. 45–56 (1998)

  113. Freitas, C.O.A., Yacoubi, A.E., Bortolozzi, F., Sabourin, R.: Brazilian bank check handwritten legal amount recognition. In: Proceedings of the 13th Brazilian Symposium on Computer Graphics and Image Processing, pp. 97–104 (2000)

  114. Kim, K.K., Kim, J., Chung, Y.K., Suen, C.Y.: Legal amount recognition based on the segmentation hypotheses for bank check processing. In: Proceedings of ICDAR, pp. 964–967 (2001)

  115. Dodel J.P., Shinghal R.: Recognition of worded amounts. In: Impedovo, S., Wang, P.S.P., Bunke, H. (eds) Automatic Bankcheck Processing, pp. 309–326. World Scientific Publishing Company, Singapore (1997)

    Google Scholar 

  116. Dodel, J.P., Shinghal, R.: Symbolic/neural recognition of cursive amounts on bank cheques. In: Proceedings of 3rd ICDAR, pp. 15–18 (1995)

  117. Tang, H., Augustin, E., Suen, C.Y., Baret, O., Cheriet, M.: Spiral recognition methodology and its application for recognition of Chinese bank checks. In: Proceedings of the 9th International Workshop on Frontiers in Handwriting Recognition, pp. 263–268 (2004)

  118. Farah N., Souici L., Sellami M.: Classifiers combination and syntax analysis for Arabic literal amount recognition. Eng. Appl. Artif. Intell. 19, 29–39 (2006)

    Article  Google Scholar 

  119. Olivier, C., Paquet, T., Avila, M., Lecourtier, Y.: Recognition of handwritten words using stochastic models. In: Proceedings of 3rd ICDAR, pp. 19–23 (1995)

  120. Shridhar, M., Houle, G.F., Kimura, F.: Comprehensive check image reader. In: Proceedings of the International Conference on Computing: Theory and Applications, pp. 407–416 (2007)

  121. Shridhar, M., Houle, G.F., Kimura, F.: Document recognition strategies for bank cheques. In: Proceedings of IEEE International Conference on Electro/Information Technology, pp. 170–173 (2009)

  122. Alemu, W., Fuchs, S.: Handwritten Amharic bank check recognition using hidden Markov random field. In: Proceedings of the Conference on Computer Vision and Pattern Recognition Workshop, pp. 28–35 (2003)

  123. Lecce, V.D., Dimauro, G., Guerriero, A., Impedovo, S., Pirlo, G., Salzo, A.: New hybrid approach for legal amount recognition. In: Proceedings of the Seventh International Workshop on Frontiers in Handwriting Recognition, pp. 199–208 (2000)

  124. Houle, G.F., Aragon, D.B., Smith, R.W., Shridhar, M., Kimura, F.: A multi-layered corroboration-based check reader. In: Proceedings of the Workshop on Document Analysis Systems, pp. 495–546 (1996)

  125. Morita, M.E., Lethelier, E., Yacoubi, A.E., Bortolozzi, F., Sabourin, R.: Recognition of handwritten dates on bankchecks using an HMM approach. In: Proceedings of XIII Brazilian Symposium on Computer Graphics and Image Processing, pp. 113–120 (2000)

  126. Morita, M., Yacoubi, A.E., Sabourin, R., Bortolozzi, F., Suen, C.Y.: Handwritten month word recognition on Brazilian bank cheques. In: Proceedings of 6th ICDAR, pp. 972–976 (2001)

  127. Kim, J., Kim, K., Nadal, C.P., Suen, C.Y.: A methodology of combining HMM and MLP classifiers for cursive word recognition. In: Proceedings of 15th ICPR, pp. 319–322 (2000)

  128. Xu, Q., Kimy, J.H., Lam, L., Sue, C.Y.: Recognition of handwritten month words on bank cheques. In: Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition, pp. 111–116 (2002)

  129. Strathy, N.W.: Handwriting recognition for cheque processing. In: Proceedings of the 2nd International Conference on Multimodal Interface, pp. 47–50 (1999)

  130. Kapp M.N., Freitas C.O.A., Sabourin R.: Methodology for the design of NN-based month-word recognizers written on Brazilian bank checks. Image Vis. Comput. 25, 40–49 (2007)

    Article  Google Scholar 

  131. Morita M., Sabourin R., Bortolozzi F., Suen C.Y.: Segmentation and recognition of handwritten dates: an HMM-MLP hybrid approach. Int. J. Document Anal. Recognit. 6, 248–262 (2004)

    Google Scholar 

  132. Dimauro, G., Grattagliano, M.R., Impedovo, S., Pirlo, G.: A system for bankcheck processing. In: Proceedings of 2nd ICDAR, pp. 454–459 (1993)

  133. Kaufmann G., Bunke H.: Detection and correction of recognition errors in check reading. Int. J. Document Anal. Recognit. 2, 211–221 (2000)

    Article  Google Scholar 

  134. Impedovo D., Pirlo G.: Automatic signature verification: the state of the art. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 38(5), 609–635 (2008)

    Article  Google Scholar 

  135. Yoshimura M., Yoshimura I.: Investigation of a verification system for Japanese countersignatures on traveler’s checks. Trans. IEICE 80(7), 1764–1773 (1997)

    Google Scholar 

  136. Impedovo, S., Pirlo, G.: Verification of handwritten signatures: an overview In: Proceedings of 14th International Conference on Image Analysis and Processing, pp. 191–196 (2007)

  137. Bajaj R., Chaudhury S.: Signature verification using multiple neural classifiers. Pattern Recognit. 30(1), 1–7 (1997)

    Article  Google Scholar 

  138. Cordella, L.P., Foggia, P., Sansone, C., Tortorella, F., Vento, M.: In: Kittler, J., Roli, F. (eds.) A Cascaded Multiple Expert System for Verification, LNCS, vol. 1857, pp. 330–339. Springer (2000)

  139. Dimauro G., Impedovo S., Pirlo G., Salzo A.: A multi-expert signature verification system for bank check processing. IJPRAI 11(5), 827–844 (1997)

    Google Scholar 

  140. Huang K., Yan H.: Offline signature verification based on geometric feature extraction and neural network classification. Pattern Recognit. 30(1), 9–17 (1997)

    Article  Google Scholar 

  141. Huang K., Yan H.: Offline signature verification using structural feature correspondence. Pattern Recognit. 35(11), 2467–2477 (2002)

    Article  MATH  Google Scholar 

  142. Santos, C., Justino, E.J.R., Bortolozzi, F., Sabourin, R.: An offline signature verification method based on the questioned document expert’s approach and a neural network classifier. In: Proceedings of 9th IWFHR, pp. 498–502 (2004)

  143. Xiao X.H., Leedham G.: Signature verification by neural networks with selective attention. Appl. Intell. 11, 213–223 (1999)

    Article  Google Scholar 

  144. Sansone C., Vento M.: Signature verification: increasing performance by a multi-stage system. Pattern Anal. Appl. 3, 169–181 (2000)

    Article  Google Scholar 

  145. Baltzakis H., Papamarkos N.: A new signature verification technique based on a two-stage neural network classifier. Eng. Appl. Artif. Intell. 14, 95–103 (2001)

    Article  Google Scholar 

  146. Armand, S., Blumenstein, M., Muthukkumarasamy, V.: Off-line signature verification based on the modified direction feature. In: Proceedings of 18th ICPR, pp. 509–512 (2006)

  147. http://en.wikipedia.org/wiki/Dynamic_time_warping

  148. Deng P.S., Liao H.M., Ho C.W., Tyan H.R.: Wavelet-based off-line handwritten signature verification. Comput. Vis. Image Underst. 76(3), 173–190 (1999)

    Article  Google Scholar 

  149. Fang B., Leung C.H., Tang Y.Y., Tse K.W., Kwok P.C.K., Wong Y.K.: Offline signature verification by the tracking of feature and stroke positions. Pattern Recognit. 36(1), 91–101 (2003)

    Article  MATH  Google Scholar 

  150. Shanker A.P., Rajagopalan A.N.: Off-line signature verification using DTW. Pattern Recognit. Lett. 28(12), 1407–1414 (2007)

    Article  Google Scholar 

  151. Jayadevan R., Kolhe S.R., Patil P.M.: Dynamic time warping based static hand printed signature verification. J. Pattern Recognit. Res. 4(1), 52–65 (2009)

    Google Scholar 

  152. Nel E.M., Preez J.A., Herbst B.M.: Estimating the pen trajectories of static signatures using hidden Markov models. IEEE Trans. Pattern Anal. Mach. Intell. 27(11), 1733–1746 (2007)

    Article  Google Scholar 

  153. Justino, E.J.R., Bortolozzi, F., Sabourin, R.: Off-line signature verification using HMM for random, simple and skilled forgeries. In: Proceedings of 6th ICDAR, pp. 1031–1034 (2001)

  154. Coetzer J., Herbst B.M., du Preez J.A.: Offline signature verification using the discrete radon transform and a hidden Markov model EURASIP. J. Appl. Signal Process. 4, 559–571 (2004)

    Article  Google Scholar 

  155. Coetzer, J., Herbst, B.M., du Preez, J.A.: Off-line signature verification: a comparison between human and machine performance. In: Proceedings of 10th IWFHR (2006)

  156. Batista L., Granger E., Sabourin R.: Improving performance of HMM-based off-line signature verification systems through a multi-hypothesis approach. IJDAR 13, 33–47 (2010)

    Article  Google Scholar 

  157. http://en.wikipedia.org/wiki/Fuzzy_logic

  158. Velez J., Sanchez A., Moreno B., Esteban J.L.: Fuzzy shape-memory snakes for the automatic off-line signature verification problem. Fuzzy Sets Syst. 160, 182–197 (2009)

    Article  MathSciNet  Google Scholar 

  159. Madasu, V.K., Hafizuddin, M., Yusof, M., Hanmandlu, M., Kubik, K.: In: Gedeon, T.D., Fung, L.C.C. (eds.) Offline Signature Verification and Forgery Detection System Based on Fuzzy Modeling, LNAI 2903, pp. 1003–1013. Springer (2003)

  160. Ismail M.A., Gad S.: Off-line Arabic signature recognition and verification. Pattern Recognit. 33, 1727–1740 (2000)

    Article  Google Scholar 

  161. Quek C., Zhou R.W.: Antiforgery: a novel pseudo-outer product based fuzzy neural network driven signature verification system. Pattern Recognit. Lett. 23, 1795–1816 (2002)

    Article  MATH  Google Scholar 

  162. Hanmandlu M., Yusof M.H.M., Madasu V.K.: Off-line signature verification and forgery detection using fuzzy modeling. Pattern Recognit. 38, 341–356 (2005)

    Article  MATH  Google Scholar 

  163. http://en.wikipedia.org/wiki/Support_vector_machine

  164. Hairong L., Wang W., Wang C., Zhuo Q.: Off-line Chinese signature verification based on support vector machines. Pattern Recognit. Lett. 26, 2390–2399 (2005)

    Article  Google Scholar 

  165. Vargas J.F., Ferrer M.A., Travieso C.M., Alonso J.B.: Off-line signature verification based on grey level information using texture features. Pattern Recognit. 44, 375–385 (2011)

    Article  MATH  Google Scholar 

  166. Bertolini D., Oliveira L.S., Justino E., Sabourin R.: Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers. Pattern Recognit. 43, 387–396 (2010)

    Article  MATH  Google Scholar 

  167. Batista, L., Granger, E., Sabourin, R.: Applying dissimilarity representation to off-line signature verification. In: Proceedings of ICPR, pp. 1293–1297 (2010)

  168. Vargas, J.F., Ferrer, M.A., Travieso, C.M., Alonso, J.B.: Offline signature verification based on pseudo-cepstral coefficients. In: Proceedings of 10th ICDAR, pp. 126–130 (2009)

  169. Nguyen, V., Blumenstein, M., Muthukkumarasamy, V., Leedham, G.: Off-line signature verification using enhanced modified direction features in conjunction with neural classifiers and support vector machines. In: Proceedings of ICDAR-2007, pp. 734–738

  170. Nguyen, V., Blumenstein, M., Leedham, G.: Global features for the off-line signature verification problem. In: Proceedings of ICDAR-2009, pp. 1300–1304

  171. Nguyen, V., Kawazoe, Y., Wakabayashi, T., Pal, U., Blumenstein, M.: Performance analysis of the gradient feature and the modified direction feature for off-line signature verification. In: Proceedings of 12th ICFHR, pp. 303–307 (2010)

  172. http://en.wikipedia.org/wiki/Naive_Bayes_classifier

  173. Xiao X., Leedham G.: Signature verification using a modified Bayesian network. Pattern Recognit. 35, 983–995 (2002)

    Article  MATH  Google Scholar 

  174. Ruiz-del-Solar, J., Devia, C., Loncomilla, P., Concha, F.: Offline signature verification using local interest points and descriptors. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds.) LNCS 5197, pp. 22–29 (2008)

  175. Kalera M.K., Srihari S., Xu A.: Offline signature verification and identification using distance statistics. IJPRAI 18(7), 1339–1360 (2004)

    Google Scholar 

  176. Fang, B., Tang, Y.Y.: In: Zhang, D., Jain, A.K. (eds.) Reduction of Feature Statistics Estimation Error for Small Training Sample Size in Offline Signature Verification, LNCS 3072, pp. 526–532. Springer (2004)

  177. Mizukami, Y., Yoshimura, M., Miike, H., Yoshimura, I.: An offline signature verification system using an extracted displacement function. In: Proceedings of 5th ICDAR, pp. 757–760 (1999)

  178. Ramesh V.E., Murty M.N.: Offline signature verification using genetically optimized weighted features. Pattern Recognit. 32(2), 217–233 (1999)

    Article  Google Scholar 

  179. Ueda, K.: Investigation of offline Japanese signature verification using a pattern matching. In: Proceedings of 7th ICDAR, pp. 951–955 (2003)

  180. Hairong, L., Bai, X., Yin, W., Dong, J.: Off-line signature verification using graphical model. In: Proceedings of 20th ICPR, pp. 3784–3788 (2010)

  181. Chen, S., Srihari, S.: A new off-line signature verification method based on graph matching. In: Proceedings of 18th ICPR, pp. 869–872 (2006)

  182. Sabourin, R., Genest, G., Preteux, F.: Pattern spectrum as a local shape factor for off-line signature verification. In: Proceedings of International Conference on Pattern Recognition, pp. 43–48 (1996)

  183. Sabourin R., Genest G., Preteux F.: Off-line signature verification by local granulometric size distributions. IEEE Trans. Pattern Anal. Mach. Intell. 19(9), 976–988 (1997)

    Article  Google Scholar 

  184. Sabourin, R., Drouhard, J.P., Wah, E.S.: Shape Matrices as a Mixed Shape Factor for Off-line Signature Verification. 4th ICDAR, pp. 661–665 (1997)

  185. Zimmer, A., Ling, L.L.: A Hybrid On/Off Line Handwritten Signature Verification System. 7th ICDAR-7, pp. 424–428 (2003)

  186. Zimmer, A., Ling, L.L.: A Window-based hybrid signature verification system. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004, LNCS 3072, pp. 562–568. Springer, Berlin (2004)

  187. Kimura, F., Shridhar, M., Chen, Z.: Improvements of a lexicon directed algorithm for recognition of unconstrained handwritten words. In: Proceedings of 2nd ICDAR, pp. 18–22 (1993)

  188. http://www.a2ia.com

  189. www.techinfocenter.com

  190. http://www.miteksystems.com/ImageNet_Payments_details.asp

  191. http://www.miteksystems.com/MobileDeposit.asp

  192. http://www.parascript.com/products2/CheckPlus.cfm

  193. http://www.parascript.com/products2/CheckUltra.cfm

  194. http://www.parascript.com/products2/CheckUsability.cfm

  195. http://www.signplus.com/en/solutions/fraudone.php

  196. Ohalia Y.A., Cheriet M., Suen C.Y.: Databases for recognition of handwritten Arabic cheques. Pattern Recognit. 36, 111–121 (2003)

    Article  Google Scholar 

  197. Impedovo, S., Dimauro, G., Modugno, R., Pirlo, G.: A new database for research on bank-check processing. In: Proceedings of 8th IWFHR, pp. 524–528 (2002)

  198. Zhu H., Tang X.L., Liu P.: An MLP-orthogonal Gaussian mixture model hybrid model for Chinese bank check printed numeral recognition. Int. J. Document Anal. 8(1), 27–34 (2006)

    Article  Google Scholar 

  199. Morita, M., Sabourin, R., Bortolozzi, F., Suen, C.Y.: A recognition and verification strategy for handwritten word recognition. In: Proceedings of 7th ICDAR, pp. 482–486 (2003)

  200. Chin, F., Wu, F.: A microprocessor based optical character recognition check reader. In: Proceedings of 3rd ICDAR, pp. 982–985 (1995)

  201. Ueda, K., Matsuo, K.: Automatic seal imprint verification system for bankcheck processing. In: Proceedings of the 3rd International Conference on Information Technology and Applications, pp. 768–771 (2005)

  202. Jesus, A., Facon, J.: Segmentation of Brazilian bank check logos without a priori knowledge. In: Proceedings of the International Conference on Information Technology: Coding and Computing, pp. 259–263 (2000)

  203. http://www.smartmoneydaily.com/personalfinance/15-historical-interesting-bank-checks.aspx

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, Pune Institute of Computer Technology, Pune, 411043, India

    R. Jayadevan

  2. Department of Computer Sciences, North Maharashtra University, Jalgaon, India

    S. R. Kolhe

  3. RMD Sinhgad Technical Institutes, Pune, India

    P. M. Patil

  4. CVPR Unit, Indian Statistical Institute, Kolkata, India

    U. Pal

Authors
  1. R. Jayadevan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. R. Kolhe
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. M. Patil
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. U. Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Jayadevan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jayadevan, R., Kolhe, S.R., Patil, P.M. et al. Automatic processing of handwritten bank cheque images: a survey. IJDAR 15, 267–296 (2012). https://doi.org/10.1007/s10032-011-0170-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10032-011-0170-8

Keywords

Search

Navigation