Journal of Information Processing Systems

  • 제16권1호
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  • Pages.224-237
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  • 2020
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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Low-Quality Banknote Serial Number Recognition Based on Deep Neural Network

  • Jang, Unsoo (Dept. of Computer Science, Sangmyung University) ;
  • Suh, Kun Ha (Dept. of Computer Science, Sangmyung University) ;
  • Lee, Eui Chul (Dept. of Human Centered Artificial Intelligence, Sangmyung University)
  • 투고 : 2018.01.16
  • 심사 : 2018.11.27
  • 발행 : 2020.02.29
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초록

Recognition of banknote serial number is one of the important functions for intelligent banknote counter implementation and can be used for various purposes. However, the previous character recognition method is limited to use due to the font type of the banknote serial number, the variation problem by the solid status, and the recognition speed issue. In this paper, we propose an aspect ratio based character region segmentation and a convolutional neural network (CNN) based banknote serial number recognition method. In order to detect the character region, the character area is determined based on the aspect ratio of each character in the serial number candidate area after the banknote area detection and de-skewing process is performed. Then, we designed and compared four types of CNN models and determined the best model for serial number recognition. Experimental results showed that the recognition accuracy of each character was 99.85%. In addition, it was confirmed that the recognition performance is improved as a result of performing data augmentation. The banknote used in the experiment is Indian rupee, which is badly soiled and the font of characters is unusual, therefore it can be regarded to have good performance. Recognition speed was also enough to run in real time on a device that counts 800 banknotes per minute.

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참고문헌

  1. J. Qian, D. Qian, and M. Zhang, "A digit recognition system for paper currency identification based on virtual instruments," in Proceedings of the International Conference on Information and Automation, Shandong, China, 2006, pp. 228-233.
  2. P. S. Reel, G. Krishan, and S. Kotwal, "Image processing based heuristic analysis for enhanced currency recognition," International Journal of Advancements in Technology, vol. 2, no. 1, pp. 82-89, 2011.
  3. M. Bhansali and P. Kumar, "An alternative method for facilitating cheque clearance using smart phones application," International Journal of Application or Innovation in Engineering & Management, vol. 2, no. 1, pp. 211-217, 2013.
  4. M. T. Qadri and M. Asif, "Automatic number plate recognition system for vehicle identification using optical character recognition," in Proceedings of the International Conference on Education Technology and Computer, Singapore, 2009, pp. 335-338.
  5. H. Penz, I. Bajla, A. Vrabl, W. Krattenthaler, and K. Mayer, "Fast real-time recognition and quality inspection of printed characters via point correlation," in Proceedings of SPIE 4303: Real-Time Imaging V. Bellingham, WA: International Society for Optics and Photonics, 2001, pp. 127-137.
  6. P. K. Sahoo, S. A. K. C. Soltani, and A. K. Wong, "A survey of thresholding techniques," Computer Vision, Graphics, and image Processing, vol. 41, no. 2, pp. 233-260, 1988. https://doi.org/10.1016/0734-189X(88)90022-9
  7. T. T. Zhao, J. Y. Zhao, R. R. Zheng, and L. L. Zhang, "Study on RMB number recognition based on genetic algorithm artificial neural network," in Proceedings of the International Congress on Image and Signal Processing, Yantai, China, 2010, pp. 1951-1955.
  8. B. Y. Feng, M. Ren, X. Y. Zhang, and C. Y. Suen, "Automatic recognition of serial numbers in bank notes," Pattern Recognition, vol. 47, no. 8, pp. 2621-2634, 2014. https://doi.org/10.1016/j.patcog.2014.02.011
  9. B. B. Chaudhuri and U. Pal, "A complete printed Bangla OCR system," Pattern Recognition, vol. 31, no. 5, pp. 531-549, 1998. https://doi.org/10.1016/S0031-3203(97)00078-2
  10. W. Li, W. Tian, X. Cao, and Z. Gao, "Application of support vector machine (SVM) on serial number identification of RMB," in Proceedings of the World Congress on Intelligent Control and Automation, Jinan, China, 2010, pp. 6262-6266.
  11. F. Mohammad, J. Anarase, M. Shingote, and P. Ghanwat, "Optical character recognition implementation using pattern matching," International Journal of Computer Science and Information Technologies, vol. 5, no. 2, pp. 2088-2090, 2014.
  12. M. K. Sarker and M. K. Song, "Segmentation and recognition of Korean vehicle license plate characters based on the global threshold method and the cross-correlation matching algorithm," Journal of Information Processing Systems, vol. 12, no. 4, pp. 661-680, 2016. https://doi.org/10.3745/JIPS.02.0050
  13. J. Singh, G. Singh, and R. Singh, "Optimization of sentiment analysis using machine learning classifiers," Human-centric Computing and Information Sciences, vol. 7, article no. 32, 2017.
  14. T. K. Mishra, B. Majhi, and R. Dash, "A contour descriptors-based generalized scheme for handwritten Odia numerals recognition," Journal of Information Processing Systems, vol. 13, no. 1, pp. 174-183, 2014. https://doi.org/10.3745/JIPS.02.0012
  15. J. Kaur and M. Aggarwal, "A LabVIEW approach to detect the theft of Indian currency notes," International Journal of Advanced Research in Electronics and Communication Engineering, vol. 2, no. 1, pp. 84-88, 2013.
  16. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," Advances in Neural Information Processing Systems, vol. 25, pp. 1097-1105, 2012.
  17. L. Prechelt, "Early stopping-but when?" in Neural Networks: Tricks of the Trade. Heidelberg: Springer, 1998, pp. 55-69.