Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Digit Segmentation in Digit String Image Using CPgraph

CPgraph를 이용한 숫자열 영상에서 숫자 분할

  • Oh, Jeong-su (Department of Display Engineering, Pukyong National University)
  • Received : 2019.05.25
  • Accepted : 2019.07.19
  • Published : 2019.09.30
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Abstract

In this paper, I propose an algorithm to generate an input digit image for a digit recognition system by detecting a digit string in an image and segmenting the digits constituting the digit string. The proposed algorithm detects blobbed digit string through blob detection, designates a digit string area and corrects digit string skew using the detected blob information. And the proposed algorithm corrects the digit skew and determines the boundary points for the digit segmentation in the corrected digit sequence using three CPgraphs newly defined in this paper. In digit segmentation experiments using the image group including digit strings printed with a range of the font sizes and the image group including handwritten digit strings, the proposed algorithm successfully segments 100% and 90% of the digits in each image group.

본 논문은 영상에서 숫자열을 검출하고 숫자열을 구성하고 있는 숫자들을 분할하여 숫자 인식 시스템을 위한 입력 숫자 영상을 생성하는 알고리즘을 제안하고 있다. 제안된 알고리즘은 블랍 검출을 통해 블랍화된 숫자열을 검출하고, 검출된 블랍 정보를 이용해 숫자열 영역을 지정하고, 숫자열 기울어짐을 보정한다. 그리고 제안된 알고리즘은 본 논문에서 새롭게 정의된 세 종류의 CPgraph을 이용해 숫자 기울어짐을 보정하고, 보정된 숫자열에서 숫자 분할을 위한 경계 지점을 결정한다. 일정 영역의 폰트 크기로 인쇄된 숫자열을 포함하는 영상 그룹과 필기체 숫자열을 포함하는 영상 그룹을 이용한 숫자 분할 실험에서 제안된 알고리즘 각 영상 그룹에서 100%와 90% 이상의 숫자들을 성공적으로 분할하고 있다.

Keywords

References

  1. A. Krizhevsky, I. Sutskever, and G. Hinton, "ImageNet classification with deep convolutional neural networks," Advances in Neural Information Processing Systems 25, pp. 1097-1105, 2012.
  2. S. C. Lim, and D. Y. Kim, "Apply Locally Weight Parameter Elimination for CNN Model Compression," Journal of the Korea Institute of Information and Communication Engineering, vol. 22, no. 9, pp. 1165-1171, Sep. 2018. https://doi.org/10.6109/JKIICE.2018.22.9.1165
  3. S. G. Hwang, Computer Vision and Machine Learning with OpenCV4, Gilbut Inc., Seoul, 2019.
  4. A. G. Hochuli , L. S. Oliveira , A. S. Britto and R. Sabourin, "Handwritten digit segmentation: Is it still necessary?," Pattern Recognition, vol. 78, no. 3, pp.1-11, Jun. 2018. https://doi.org/10.1016/j.patcog.2018.01.004
  5. A Gattal, Y Chibani, B Hadjadji, "Segmentation and recognition system for unknown-length handwritten digit strings," Pattern analysis & applications, vol. 20, no. 2, pp.307-323, Feb. 2017. https://doi.org/10.1007/s10044-017-0607-x
  6. S. G. Lee, K. H. Nam, and J. M. Jung, "Implementation of handwritten digit recognition CNN structure using GPGPU and Combined Layer," The Journal of the Convergence on Culture Technology, vol. 3, no. 4, pp.165-169, Nov. 2017. https://doi.org/10.17703/JCCT.2017.3.4.165
  7. J. Tian, R. Wang, G. Wang, J. Liu, Y. Xia, "A Two-Stage Character Segmentation Method for Chinese License Plate," A Computers & Electrical Engineering, vol. 46, no. 8, pp.539-553, Aug. 2015. https://doi.org/10.1016/j.compeleceng.2015.02.014
  8. MathWorks, Measure properties of image regions [internet]. Available https://kr.mathworks.com/help/images/ref/regionprops.html.
  9. A. Kaehler, and G. Bradski, Learning, OpenCV3, Wikibooks, Paju, 2018.