Abstract
Bank cheques, as documents issued by banks can be used as a form of bills capable of monetary exchange, allowing a payee a certain sum of money from the account of drawer. However, due to many fraudulent practices and a need of faster cheque clearance, there had been advances in the process of cheque clearance. Consequently, to aid the process of cheque validation this research work focuses on implementing image processing techniques such as OCR, ANN and Deep Learning to extract key parameters essential for cheque validation. These techniques can be used in sequential manner to automate the task of cheque validation. For extracting machine typographic information Optical Character Recognition is used. Whereas, for the handwritten characters we have used CNN trained using MNIST dataset. The accuracy achieved in handwritten character recognition is 99.14%. For testing purposes IDRBT cheque dataset is used comprising cheque leaflets of different banks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sabu, A.M., Das, A.N.: A survey on various optical character recognition techniques. In: International Conference on Emerging Devices and Smart Systems, pp. 152–155 (2018)
Sahare, P., Dhok, S.B.: Multilingual character segmentation and recognition schemes for Indian document images. IEEE Access 7, 10603–10607 (2018)
Li, Y., Zhang, J., Gao, P., Jiang, L., Chen, M.: Grab cut image segmentation on image region. In: 3rd International Conference on Image, Vision and Computing, pp. 311–315 (2018)
Lee, S.H., Yang, C.S., Hou, T.W., Yeh, C.H.: An image preprocessing method for fingernail segmentation in microscopy image. In: 2nd International Conference on Signal and Image Processing, pp. 489–493 (2017)
Dorgham, O.M.: Automatic body segmentation from computed tomography image. In: 3rd International Conference on Advanced Technologies, pp. 1–5 (2017)
Brzoza, A., Muszynski, G.: An approach to image segmentation based on shortest paths. In: International Conference on Systems, Signals and Image Processing, pp. 1–5 (2017)
Saddami, K., Afrah, P., Mutiawani, V., Arnia, F.: A new adaptive thresholding technique for binarizing ancient document. In: Indonesian Association for Pattern Recognition International Conference, pp. 57–61 (2017)
Agrawal, N., Kaur, A.: An algorithmic approach for text recognition from printed and typed text images. In: 8th International Conference on Cloud Computing, Data Science & Engineering, pp. 876–879 (2018)
Latha, M.A., Evangeline, N.C., SankaraNarayanan, S.: Colour image segmentation of fundus blood vessels for the detection of hypertensive retinopathy. In: 4th International Conference on Biosignals, Images and Instrumentation, pp. 206–212 (2018)
Vidakis, D.G., Kosmopolous, D.I.: Facilitation of air traffic control via optical character recognition-based aircraft registration number extraction. Inst. Eng. Technol. J. 12, 965–975 (2018)
Yelniz, I.Z., Manmatha, R.: Dependence models for searching text in document images. IEEE Trans. Pattern Anal. Mach. Intell. 41, 49–63 (2018)
Sun, Y., Mao, X., Hong, S., Xu, W., Gui, G.: Template matching-based method for intelligent invoice information identification. IEEE Access 7, 28392–28401 (2019)
Agrawal, P., Kaur, R., Madaan, V., Mukkelli, S.B., Sethi, D.: Moving object detection and recognition using optical flow and eigen face using low resolution video. In: Recent Patents on Computer Science, pp. 1–8. Bentham Science Publisher (2018)
Kaur, G., Agrawal, P.: Optimisation of image fusion using feature matching based on SIFT and RANSAC. Indian J. Sci. Technol. 9, 1–7 (2016)
Monica, Singh, S.K., Agrawal, P., Madaan, V.: Breast cancer diagnosis using digital image segmentation techniques. Indian J. Sci. Technol. 9, 1–5 (2016)
Ben-Musa, A.S., Singh, S.K., Agrawal, P.: Object detection and recognition in cluttered scene using Harris corner detection. In: International Conference on Control, Instrumentation, Communication & Computational Technologies (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chaudhary, D., Agrawal, P., Madaan, V. (2019). Bank Cheque Validation Using Image Processing. In: Luhach, A., Jat, D., Hawari, K., Gao, XZ., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2019. Communications in Computer and Information Science, vol 1075. Springer, Singapore. https://doi.org/10.1007/978-981-15-0108-1_15
Download citation
DOI: https://doi.org/10.1007/978-981-15-0108-1_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0107-4
Online ISBN: 978-981-15-0108-1
eBook Packages: Computer ScienceComputer Science (R0)