Siham Lamssaoui
ABSTRACT
In spite of speed evolution of digital techniques, a huge systems continue to rely on the use of paper as the dominant medium. Bank checks are part too and still widely used all over the world for financial transactions. In this paper, we propose a new architecture for automatic bank check recognition based on deep learning. The proposed architecture involves four stages: segment regions of interest into individual digits, normalization, recognition of each character using a convolutional neural network classifier and syntactic verification. Experimental results are presented using multiple checks from Moroccan banks to show the importance of each module by analyzing the effect over the recognition accuracy. All the modules proposed are highly configurable and can be adapted to suit the characteristics of checks from different countries.
- M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al. TensorFlow: A system for large-scale machine learning. In OSDI, volume 16, pages 265--283, 2016. Google Scholar
Digital Library
- L. Deng. The mnist database of handwritten digit images for machine learning research {best of the web}. IEEE Signal Processing Magazine, 29(6):141--142, 2012.Google ScholarCross Ref
- A. Gulli and S. Pal. Deep Learning with Keras. Packt Publishing Ltd, 2017. Google ScholarDigital Library
- S. Hoo-Chang, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging, 35(5):1285, 2016.Google Scholar
- A. Krizhevsky, I. Surskever, and G.E. Hinton, Imagenet classification with deed convolutional neural networks. In Advances in Neural information processing systems, pages 1097--1105, 2012. Google ScholarDigital Library
- Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. nature, 521(7553):436, 2015.Google Scholar
- R. Palacios, A. Sinha, and A. Gupta. Automatic processing of brazilian bank checks. Machine Vision and Applications, pages 1--28, 2002.Google Scholar
- S. J. Pan, Q. Yang, et al. A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10):1345--1359, 2010. Google ScholarDigital Library
- R. Plamondon and S.N. Srihari. Online and off-line handwriting recognition: a comprehensive survey. IEEE Transactions on pattern analysis and machine intelligence, 22(1): 63--84, 2000. Google ScholarDigital Library
- M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi. Xnor-net: ImageNet classification using binary convolutional neural networks. In European Conference on Computer Vision, pages 525--542. Springer, 2016.Google ScholarCross Ref
- N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. Dropout: A simple way to prevent neural networks from overfiting. The journal Machine Learning Research. 15(1): 1929--1958, 2014. Google ScholarDigital Library
- Z. Tian, W. Huang, T. He, P. He, and Y. Qiao. Detecting text in natural image with connectionist text proposal network. In European conference on computer vision, pages 56--72. Springer, 2016.Google ScholarCross Ref
- P.S.-P. Wang and Y. Zhang, A fast and flexible thinning algorithm. IEEE Transactions on Computers, 38(5): 741--745, 1989. Google ScholarDigital Library
- L. Xu, A. Krzyzak, and C.Y. Suen. Methods of combining multiple classifiers and their applications to handwriteing recognition. IEEE Transactions on systems, man, and cybernetics, 22(3): 418--435, 1992.Google ScholarCross Ref
- L. Yann, C. Corinna, and J. Christopher. The mnist database of handwritten digits. URL http://yhann. lecun.com/exdb/mnist, 1998.Google Scholar
- S. Zagoruyko and N. Komodakis. Wide residual networks. arXiv preprint arXiv:1605.07146, 2016.Google Scholar
Index Terms
- Novel Automatic Bank Check Recognition Based on Deep Learning
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