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An Efficient Pattern Recognition Technology for Numerals of Lottery and Invoice

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Genetic and Evolutionary Computing (ICGEC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1107))

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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.

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References

  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. 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. 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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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 2017

    Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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 

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Acknowledgments

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

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Authors and Affiliations

  1. Department of Electrical Engineering, National Changhua University of Education, Changhua, 500, Taiwan

    Yi-Nung Chung, Ming-Sung Chiu, Chien-Chih Lin & Jhen-Yang Wang

  2. Department of Information and Network Communications, Chienkuo Technology University, Changhua, 500, Taiwan

    Chao-Hsing Hsu

Authors
  1. Yi-Nung Chung
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  2. Ming-Sung Chiu
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  3. Chien-Chih Lin
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  4. Jhen-Yang Wang
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  5. Chao-Hsing Hsu
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Corresponding author

Correspondence to Chao-Hsing Hsu .

Editor information

Editors and Affiliations

  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

    Jeng-Shyang Pan

  2. Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  3. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

    Yongquan Liang

  4. School of Computer Science, Engineering and Mathematics, Flinders University, Bedford Park, Australia

    Shu-Chuan Chu

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Chung, YN., Chiu, MS., Lin, CC., Wang, JY., Hsu, CH. (2020). An Efficient Pattern Recognition Technology for Numerals of Lottery and Invoice. In: Pan, JS., Lin, JW., Liang, Y., Chu, SC. (eds) Genetic and Evolutionary Computing. ICGEC 2019. Advances in Intelligent Systems and Computing, vol 1107. Springer, Singapore. https://doi.org/10.1007/978-981-15-3308-2_28

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