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An Efficient Numeric Character Segmentation of Metering Devices for Remote Automatic Meter Reading

원격 자동 검침을 위한 효과적인 계량기 숫자 분할

  • 보반 토안 (숭실대학교 대학원 정보통신공학과) ;
  • 정선태 (숭실대학교 정보통신전자공학부) ;
  • 조성원 (홍익대학교 전자전기공학부)
  • Received : 2012.02.10
  • Accepted : 2012.04.16
  • Published : 2012.06.30

Abstract

Recently, in order to support automatic meter reading for conventional metering devices, an image processing-based approach of recognizing the number meter data in the captured meter images has attracted many researchers' interests. Numerical character segmentation is a very critical process for successful recognition. In this paper, we propose an efficient numeric character segmentation method which can segment numeric characters well for any metering device types under diverse illumination environments. The proposed method consists of two consecutive stages; detection of number area containing all numbers as a tight ROI(Region of Interest) and segmentation of numerical characters in the ROI. Detection of tight ROI is achieved in two steps: extraction of rough ROI by utilizing horizontal line segments after illumination enhancement preprocessing, and making the rough ROI more tight through clipping utilizing vertical and horizontal projection about binarized ROI. Numerical character segmentation in the detected ROI is stably achieved in two processes of 'vertical segmentation of each number region' and 'number segmentation in the each vertical segmented number region'. Through the experiments about a homegrown meter image database containing various meter type images of low contrast, low intensity, shadow, and saturation, it is shown that the proposed numeric character segmentation method performs effectively well for any metering device types under diverse illumination environments.

최근 들어, 기존 계량기에서의 원격 자동 검침을 지원하기 위한 영상 기반 계량기 데이터 숫자 인식에 대한 관심이 증대되고 있다. 성공적인 숫자 인식을 달성하는 데 숫자 분할은 매우 중요한 과정이다. 본 논문에서는 다양한 조명하의 다양한 계량기들에 대해서 잘 수행되는 효과적인 계량기 숫자 분할 방법을 제안한다. 제안된 계량기 숫자 분할 방법은 먼저 계량기 전체 숫자 영역을 정교한 관심영역으로 검출하고, 이후 검출된 관심영역에서 각 숫자를 분할하는 2단계로 구성된다. 정교한 관심영역 검출은 조명 개선 전처리 후에 수평 라인 세그먼트를 이용한 개략적 관심영역 추출, 이진화후 수직 및 수평 투영을 이용한 클리핑을 통한 개략 관심영역 정교화 등의 과정으로 처리된다. 검출된 관심영역에서의 숫자 분할은 '숫자 구역 수직 분할' 및 '수직 분할된 각 숫자 구역에서의 숫자 분할' 등의 2개 과정을 통해 안정적으로 분할되도록 처리된다. 저대비, 저저도, 음영, 포화 등 다양한 조명 환경하의 다양한 계량기 종류에 대해 직접 촬영하여 자체 제작한 계량기 이미지 데이터베이스에 기반한 실험을 통해 본 논문에서 제안한 숫자 분할 방법을 평가하고, 제안방법이 다양한 조명 환경하의 다양한 계량기 타입에 대해서 계량기 숫자를 효과적으로 잘 분할함을 확인하였다.

Keywords

References

  1. M. Hashmi, S. Hanninen, and 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. S. Zhao, B. Li, J. Yuan, and G. Cui., "Research on Remote Meter Automatic Reading Based on Computer Vision," Proc. IEEE/PES Conf. on Transmission and Distribution, pp. 1-4, 2005.
  3. D. Shu, S. Ma, and C. Jing, "Study of the Automatic Reading of Watt Meter Based on Image Processing Technology," Proc. IEEE Conf. on Industrial Electronics and Applications, pp. 2214-2217, 2007.
  4. Z. Zhang and Y. Li, "Research on the Preprocessing Method of Automatic Reading Water Meter System," Int' Conf. on Artificial Intelligence and computational Intelligence, Vol.3, pp. 549-553, 2009.
  5. X. Rui and X. Song, "A Character Recognition Algorithm Adapt to a Specific Kind of Water Meter," Proc. CSIE, pp. 632-636. 2009.
  6. J. Ulyate and R. Wolhuter, "Automated Reading of High Volume Water Meters," Southern Africa Telecommunication Networks and Applications Conference (SATNAC) , pp. 1-5, 2010.
  7. Q. Bai, Y. Zhang, L. Zhao, and Z. Qi, "Research of Automatic Recognition of Digital Meter Reading Based on Intelligent Image Processing," International Conference on Computer Engineering and Technology (ICCET) , Vol.5, pp. 619-623, 2010.
  8. M. Bin, M. Xiangbin, M. Xiaofu, L. Wufeng, and H. Linchong, "Digital Recognition Based On Image Device Meters," WRI Global Congress on Intelligent Systems, pp. 326-330, 2010.
  9. C.E. Anagnostopoulos, I.E. Psoroulas, I.D. Loumos, and V. Kayafas, "License Plate Recognition from Still Images and Video Sequences: A Survey," IEEE Transactions on Intelligent Transportation System, Vol.9, No. 3, pp. 377-391, 2008. https://doi.org/10.1109/TITS.2008.922938
  10. W. Yutao, T. Ruixia, M. Ling, and Y. Gang, "License Plate Character Segmentation from Video Images: A Survey," Proc. Chinese Control and Decision Conference (CCDC) , pp. 25-30, 2011.
  11. 오복진, 최두현, "색상과 배치 정보를 이용한 번호판 숫자 영역 추출," 멀티미디어학회논문지, 제14권 제9호, pp.1117-1124, 2011. https://doi.org/10.9717/kmms.2011.14.9.1117
  12. R. Gross and V. Brajovic, "An Image Preprocessing Algorithm for Illumination Invariant Face Recognition," Int' Conf. on Audio and Video-Based Biometric Person Authentication, Vol.2688, pp. 10-18, 2003.
  13. J. McCann, "Lessons Learned from Mondrians Applied to Real Images and Color Gamuts," Proc. IS&T/SID Seventh Color Imaging Conf., pp. 1-8, 1999.
  14. R.G. von Gioi, J. Jakubowicz, J.-M. Morel, and G. Randall, "LSD: A Fast Line Segment Detector with a False Detection Control," IEEE PAMI , Vol.32, No.4, pp. 722-732, 2010. https://doi.org/10.1109/TPAMI.2008.300
  15. M. Sezgin and B. Sankur, "Survey over Image Thresholding Techniques and Quantitative Performacne Evaluation," J . Electronic Imaging, Vol.13, No.1, pp. 146-165, 2004. https://doi.org/10.1117/1.1631315
  16. M. Satish, V.L. Lajish, and S.K. Kopparapu, "Edge Assisted Fast Binarization Scheme for Improved Vehicle License Plate Recognition," Proc. National Conf. Communications, pp. 1-5, 2011.
  17. L.G. Shapiro and G.C. Stockman, Computer Vision, Prentice-Hall, Upper Saddle River, NJ., 2001.
  18. T. B. Nguyen and S-T. Chung, "An Improved Real-time Blob Detection for Visual Surveillance," International Congress on Image and Signal Processing, pp. 1-5, 2009.
  19. PC1030N Datasheet, http://www.pixelplus.com/products/analog_sensor.php, 2010.
  20. ImageARM(PK1002) Datasheet, http://www.pixelplus.com/products/system_on_a_chip.php, 2010.

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