Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Meter Numeric Character Recognition Using Illumination Normalization and Hybrid Classifier

조명 정규화 및 하이브리드 분류기를 이용한 계량기 숫자 인식

  • Oh, Hangul (School of Electronic and Electrical Engineering, Hongik University) ;
  • Cho, Seongwon (School of Electronic and Electrical Engineering, Hongik University) ;
  • Chung, Sun-Tae (School of Electronic Engineering, Soongsil University)
  • 오한글 (홍익대학교 전기정보제어공학과) ;
  • 조성원 (홍익대학교 전기정보제어공학과) ;
  • 정선태 (숭실대학교 정보통신전자공학부)
  • Received : 2013.12.20
  • Accepted : 2014.02.18
  • Published : 2014.02.25
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Abstract

In this paper, we propose an improved numeric character recognition method which can recognize numeric characters well under low-illuminated and shade-illuminated environment. The LN(Local Normalization) preprocessing method is used in order to enhance low-illuminated and shade-illuminated image quality. The reading area is detected using line segment information extracted from the illumination-normalized meter images, and then the three-phase procedures are performed for segmentation of numeric characters in the reading area. Finally, an efficient hybrid classifier is used to classify the segmented numeric characters. The proposed numeric character classifier is a combination of multi-layered feedforward neural network and template matching module. Robust heuristic rules are applied to classify the numeric characters. Experiments using meter image database were conducted. Meter image database was made using various kinds of meters under low-illuminated and shade-illuminated environment. The experimental results indicates the superiority of the proposed numeric character recognition method.

본 논문에서는 저조도 및 음영이 생기는 조명 환경하에서 성능이 개선된 계량기 숫자 인식 방법을 제안한다. 저조도 및 음영 문제를 해결하기 위해 LN(Local Normalization) 처리 기법을 이용한 조명 정규화를 수행한 후, 계량기 숫자 영역 검출과 3단계 계량기 숫자 분할이 이루어진다. 마지막으로 분할된 숫자 데이터를 분류하기 위한 하이브리드 숫자 분류기가 적용된다. 제안된 하이브리드 숫자 분류기는 역전파 신경망과 템플레이트 매칭의 연속 결합으로 이루어지고, 계량기 숫자 분류에 보다 강인한 휴리스틱 규칙에 의해 최종적으로 숫자를 분류한다. 저조도 및 음영 조명 환경하의 다양한 계량기 종류에 대해 직접 촬영하여 자체 제작한 계량기 이미지 데이터베이스에 기반한 실험을 통해 본 논문에서 제안한 숫자 인식 방법을 평가하고, 제안된 계량기 숫자 인식 방법이 효과적으로 잘 동작함을 확인하였다.

Keywords

References

  1. M. Hashmi, S. Hanninen, K. Maki, "Survey of Smart Grid Concepts, Architectures, and Technological Dmonstrations Worldwide", Proc. IEEE PES Conf. on Innovative Smart Grid Technologies, pp. 1-7, 2011.
  2. X. Xiong, B. J. Choi, "Design of a Korean Character Vehicle Licence Plate Recognition System", Journal of Korean Institute of Intelligent Systems, vol. 20, no. 2, pp. 262-266, 2010. https://doi.org/10.5391/JKIIS.2010.20.2.262
  3. S. H. Lee, S. Y. Choi, S. Y. Lee, Y. S. Kim, "Licence Plate Recognition Using Improved IAFC Fuzzy Neural Network", Journal of Korean Institute of Intelligent Systems, vol. 19, no. 1, pp. 6-12, 2009. https://doi.org/10.5391/JKIIS.2009.19.1.006
  4. X. Xie, K. Lam, "An Efficient Illumination Normalization Method for Face Recognition", Pattern Recognition Letters, vol. 27, issue 6, pp. 609-617, 2006. https://doi.org/10.1016/j.patrec.2005.09.026
  5. R. G. von Gioi, et al. "LSD: A Fast Line Segment Detector with a False Detection Control", IEEE PAMI, vol. 32, no. 4, pp. 722-732, 2010.
  6. M. Stish, V. L. Lajish, S. K. Kopparapu, "Edge Assisted Fast Binarization Scheme for Improved Vehicle License Plate Recognition", 2011 National Conference on Communications(NCC), pp. 1-5, 2011.
  7. V. V. Toan et al, "An Efficient Numeric Character Segmentation of Metering Devices for Reomote Automatic Metering Reading", Journal of Korea Multimedia Society, vol. 15, no. 6, 2012.
  8. T. B. Nguyen, S. Chung, "An Improved Real-time Blob Detection for Visual Surveilance," 2nd International Congress on Image and Signal Processing, Tianjin, 2009.
  9. Y. Kim, O. Kwon, Y. Cho, K. Seo, "Genetic Programming based Illumination Robust and Non-parametric Multi-colors Detection Model", Journal of Korean Institute of Intelligent Systems, vol. 20, no. 6, pp. 780-785, 2010. https://doi.org/10.5391/JKIIS.2010.20.6.780
  10. S. J. Hong, Y. H. Cho, "Image Recognition by Using Hybrid Coefficient Measure of Correlation and Distance", Journal of Korean Institute of Intelligent Systems, vol. 20, no. 3, pp. 343-347, 2010. https://doi.org/10.5391/JKIIS.2010.20.3.343
  11. S. J. Oh, C. W. Park, "Hierarchical Clustering of Symbolic Objects based on Asymmetric Proximity", Journal of Korean Institute of Intelligent Systems, vol. 22, no. 6, pp. 729-734, 2012. https://doi.org/10.5391/JKIIS.2012.22.6.729
  12. H. S. Shin, Y. H. Cho, "An Improvement of Recognition Performance Based on Nonlinear Equalization and Statistical Correlation", Journal of Korean Institute of Intelligent Systems, vol. 22, no. 5, pp. 555-562, 2012. https://doi.org/10.5391/JKIIS.2012.22.5.555
  13. D. H. Paek, S. S. Han, "Numeric Pattern Recognition Using Genetic Algorithm and DNA Coding", Journal of Korean Institute of Intelligent Systems, vol. 13, no. 1, pp. 37-44, 2003. https://doi.org/10.5391/JKIIS.2003.13.1.037
  14. G. Bradski, "The Open CV Library", Dr. Dobb's Journal of Software Tools, 2000.
  15. K. S. Siddharth, M. Jangid, R. Dhir, R. Rani, "Handwritten Gurmukhi Character Recognition Using Statistical and Background Directional Distribution Feature", International Journal on Computer Science and Engineering, vol. 3, no. 6, pp. 2332-2345, 2001.
  16. L. Fausett, Fundamentals of Neutral Network, 1993.
  17. S. K. Lam, C. Y. Yeong, C. T.Yew, W. S. Chai, S. A. Suandi, "A Study on Similarity Computations in Template Matching Technique for Identity Verification", International Journal on Computer Science and Engineering, vol. 2, no. 8, pp. 2659-2665, 2010.
  18. PC1030NDatasheet, http://www.pixelplus.com/products/analog_sensor.php