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An Efficient Pattern Recognition Technology for Numerals of Lottery and Invoice

  • Yi-Nung Chung
  • Ming-Sung Chiu
  • Chien-Chih Lin
  • Jhen-Yang Wang
  • Chao-Hsing HsuEmail author
Conference paper
  • 23 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1107)

Abstract

An efficient pattern recognition technology is applied to recognize the number character of lottery and invoice is proposed in this paper. There are some Arabic numerals of lottery or invoice tickets easy to be confused because of unclear printing. In this algorithm, an image processing and pattern recognition technology is applied. The advantage of this algorithm includes that the region of interest for images can be captured automatically and the accuracy of recognition is remarkable. In order to compare the Arabic numerals of invoice with template in database, the optical character recognition technology is proposed. According to compare the normalized character with template in database, the algorithm can recognize the correct Arabic numerals of lottery or invoice tickets. Based on experimental results, the proposed algorithm in this paper is efficient and has high accuracy.

Keywords

Pattern recognition Arabic numerals Image processing Optical character recognition technology 

Notes

Acknowledgments

This work was supported by the Ministry of Science and Technology under Grant MOST 106-2221-E-018-028-.

References

  1. 1.
    Vu, H., Le, T.L., Tran, T.H.: A vision-based method for automatizing tea shoots detection. In: 2013 IEEE International Conference on Image Processing, pp. 3775–3779 (2013)Google Scholar
  2. 2.
    Huseyin, O., Chen, T., Wu, H.R.: Performance evaluation of multiple regions-of-interest query for accessing image databases. In: Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech, pp. 300–303 (2001)Google Scholar
  3. 3.
    Liu, F., Liu, X., Chen, Y.: An efficient detection method for rare colored capsule based on RGB and HSV color space. In: 2014 IEEE International Conference on Granular Computing, pp. 175–178 (2014)Google Scholar
  4. 4.
    Yitzhaky, Y., Peli, E.: A method for objective edge detection evaluation and detector parameter selection. IEEE Trans. Pattern Anal. Mach. Intell. 25(8), 1027–1033 (2003)CrossRefGoogle Scholar
  5. 5.
    Dezert, J., Liu, Z.G., Mercier, G.: Edge detection in color images based on DSmT. In: Proceedings of the 14th International Conference on Information Fusion (FUSION), pp. 343–350 (2011)Google Scholar
  6. 6.
    Qiu, T., Yan, Y., Gang, L.: An auto-adaptive edge-detection algorithm for flame and fire image processing. IEEE Trans. Instr. Meas. 61(5), 1486–1493 (2012)CrossRefGoogle Scholar
  7. 7.
    Jiang, J.A., Chuang, C.L., Lu, Y.L., Fahn, C.S.: Mathematical-morphology-based edge detectors for detection of thin edges in low-contrast regions. IET Image Process. 1(3), 269–277 (2007)CrossRefGoogle Scholar
  8. 8.
    Yeh, M.T., Chung, Y.N., Huang, Y.X., Lai, C.W., Juang, D.J.: Applying adaptive LS-PIV with dynamically adjusting detection region approach on the surface velocity measurement of river flow. In: Computers and Electrical Engineering, pp. 1–17, December 2017Google Scholar
  9. 9.
    Shih, H.-C., Liu, E.-R.: Automatic reference color selection for adaptive mathematical morphology and application in image segmentation. IEEE Trans. Image Process. 25(10), 4665–4676 (2016)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Zhai, X., Bensaali, F., Sotudeh, R.: Real-time optical character recognition on field programmable gate array for automatic number plate recognition system. IET Circ. Devices Syst. 7(6), 337–344 (2013)CrossRefGoogle Scholar
  11. 11.
    Ramiah, S., Liong, T.Y., Jayabalan, M.: Detecting text based image with optical character recognition for English translation and speech using Android. In: IEEE Student Conference on Research and Development (SCOReD), pp. 272–277 (2015)Google Scholar
  12. 12.
    Chung, Y.-N., Yun-Jhong, H., Tsai, X.-Z., Hsu, C.-H., Lai, C.-W.: Applying image processing technology to region area estimation. Adv. Intell. Syst. Comput. 579, 77–83 (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Yi-Nung Chung
    • 1
  • Ming-Sung Chiu
    • 1
  • Chien-Chih Lin
    • 1
  • Jhen-Yang Wang
    • 1
  • Chao-Hsing Hsu
    • 2
    Email author
  1. 1.Department of Electrical EngineeringNational Changhua University of EducationChanghuaTaiwan
  2. 2.Department of Information and Network CommunicationsChienkuo Technology UniversityChanghuaTaiwan

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