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Extracting the Slope and Compensating the Image Using Edges and Image Segmentation in Real World Image

실세계 영상에서 경계선과 영상 분할을 이용한 기울기 검출 및 보정

  • Paek, Jaegyung (Gyeongsang Univ., Graduate School of Education, Dept. of Computer Education) ;
  • Seo, Yeong Geon (Gyeongsang Univ., Dept. of Computer Science and Graduate School of CCBM)
  • Received : 2016.09.30
  • Accepted : 2016.10.30
  • Published : 2016.10.31

Abstract

In this paper, we propose a method that segments the image, extracts its slope and compensate it in the image that text and background are mixed. The proposed method uses morphology based preprocessing and extracts the edges using canny operator. And after segmenting the image which the edges are extracted, it excludes the areas which the edges are included, only uses the area which the edges are included and creates the projection histograms according to their various direction slopes. Using them, it takes a slope having the greatest edge concentrativeness of each area and compensates the slope of the scene. On extracting the slope of the mixed scene of the text and background, the method can get better results as 0.7% than the existing methods as it excludes the useless areas that the edges do not exist.

본 논문에서는 문자열과 배경이 혼합된 장면에서 영상을 분할하여 기울기를 추출하고 보정하는 방법을 제안한다. 제안된 방법은 모폴로지를 이용하여 전처리를 하고 캐니 연산자를 이용하여 경계선을 검출한다. 그리고 경계선이 검출된 영상을 분할하여 경계선이 포함되어 있지 않는 영역은 배제하고 경계선이 포함되어 있는 영역만을 이용하여 여러 방향의 기울기에 따른 투영 히스토그램을 생성한다. 이를 이용하여 각 영역의 최대 경계선 집중도를 갖는 기울기를 구하고 장면의 기울기를 보정한다. 문자열과 배경이 혼합된 장면의 기울기 검출에서 제안된 방법은 경계선이 없는 무의미한 부분을 배제하기 때문에 기존의 방법보다 0.7% 더 좋은 결과를 얻을 수 있었다.

Keywords

References

  1. P, Malathi, "Skew Detection based on Bounding Edge Approximation", IOSR Journal of Computer Engineering, Volume 16, pp136-139, 2014. https://doi.org/10.9790/0661-1657136139
  2. Brodicc, Drako, "The evaluation of the initial skewrate for printed text", Journal of Electrical Engineering, 62.3, pp134-140, 2011. https://doi.org/10.2478/v10187-011-0022-2
  3. Y, Ishitani, "Document Skew Detection Based on Local Region Complexity," ICDAR, pp. 49-52, 1993.
  4. Ju, Jae-hyon, et al., "Skew Correction of Document Images using Edge.", Journal of the Korea Institute of Information and Communication Engineering, 16. 7, pp.1487-1494, 2012. https://doi.org/10.6109/jkiice.2012.16.7.1487
  5. Upadhyay, Nishchal Gyan, and Kamlesh Lakhwani, "Edge Detection Using Fuzzy Approach Involving Automatic Threshold Generation.", International Journal Of Scientific & Techonology Research Vol. 2, Iss. 7, pp.128-131, July 2013.
  6. Canny, John, "A computational approach to edge detection." IEEE Trans. Pattern Anal. Mach. Intell., vol. 8, pp.679-698, 1986.
  7. Fang, Mei, et al., "The study on an application of otsu Operator in canny operator." International Symposium on Information Processing (ISIP). 2009.
  8. Lee, Chang-Young, et al., "A Study on Mask-based Edge Detection Algorithm using Morphology.", Journal of the Korea Institute of Information and Communication Engineering, 19.10, pp.2441-2449, 2015. https://doi.org/10.6109/jkiice.2015.19.10.2441
  9. Woo, Chong-Ho, "Image Preprocessing in Container Identifier Recognition System Using Multiple Thres hold Regions.", Journal of Korea Multimedia Society, 16.5, pp.549-557, 2013. https://doi.org/10.9717/kmms.2013.16.5.549

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