Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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MSER-based Character detection using contrast differences in natural images

자연 이미지에서 명암차이를 이용한 MSER 기반의 문자 검출 기법

  • Kim, Jun Hyeok (Dept of Plasma Bio Display, KwangWoon University) ;
  • Lee, Sang Hun (Ingenium College of Liberal Arts, KwangWoon University) ;
  • Lee, Gang Seong (Ingenium College of Liberal Arts, KwangWoon University) ;
  • Kim, Ki Bong (Department of computer information, Daejeon health institute of technology)
  • 김준혁 (광운대학교 플라즈마바이오디스플레이학과) ;
  • 이상훈 (광운대학교 인제니움학부대학) ;
  • 이강성 (광운대학교 인제니움학부대학) ;
  • 김기봉 (대전보건대학교 컴퓨터정보학과)
  • Received : 2019.03.15
  • Accepted : 2019.05.20
  • Published : 2019.05.28
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Abstract

In this paper, we propose a method to remove the background area by analyzing the pattern of the character area. In the character detection result of the MSER(Maximally Stable External Regions) method which distinguishes a region having a constant contrast background regions were detected. To solve this problem, we use the MSER method in natural images, the background is removed by calculating the change rate by searching the character area and the background area which are not different from the areas where the contrast values are different from each other. However, in the background removed image, using the LBP(Local Binary Patterns) method, the area with uniform values in the image was determined to be a character area and character detection was performed. Experiments were carried out with simple images with backgrounds, images with frontal characters, and images with slanted images. The proposed method has a high detection rate of 1.73% compared with the conventional MSER and MSER + LBP method.

본 논문에서는 문자 영역의 패턴을 분석하여 배경 영역을 제거하는 방법을 제안하였다. 명암이 일정한 영역을 구분하는 MSER(Maximally Stable External Regions)방법의 문자 검출에서는 배경 영역이 포함되어 검출되었다. 이러한 문제점을 해결하기 위해 자연 이미지에서 MSER 방법을 사용하여 명암 값이 차이가 나는 영역과 차이가 나지 않는 영역 즉 문자 영역과 배경 영역을 구해 변화율을 계산하여 배경을 제거하였다. 그러나 배경이 제거된 이미지에서 일부 제거되지 않는 배경 영역이 생겨 LBP(Local Binary Patterns)방법을 사용하여 이미지에서 균일한 값을 갖는 영역을 문자 영역이라고 판단하고 문자를 검출하였다. 실험 데이터는 배경이 단순한 이미지, 문자가 정면으로 구성된 이미지, 문자가 기울어진 이미지 등의 다양한 자연 이미지를 실험하였다. 제안하는 방법을 기존의 MSER, MSER+LBP 방법의 문자 검출 방법과 비교하였을 때 약 1.73%로 높은 검출률을 보였다.

Keywords

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Fig. 1. Example of MSER Result (a) Original Image (b) Result of MSER

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Fig. 2. Flowchart of proposed text detection image

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Fig. 3. Noise Removal Result (a) Original Image (b) MSER Result (c) Noise Removal Result

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Fig. 4. Measurement of change rate using contrastdifference

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Fig. 5. Remove Non Text Areas Result (a) Original Image (b) MSER Result (c) Remove Non Text Areas Result

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Fig. 6. Text detection using texture information Result (a) Remove Non Text Areas Result (b) Result of LBP (c) Background removal using LBP

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Fig. 7. Text detection Result (a) Remove Non Text Areas Result (b) Background removal using LBP (c) Text detection Result

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Fig. 8. ICDAR 2017 Data Set Image

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Fig. 9. Image of experiment result (a) Original Image (b) MSER Result (c) MSER+LBP Result (d) Proposed method

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Fig. 10. Image of experiment result (a) Original Image (b) MSER Result (c) MSER+LBP Result (d) Proposed method

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Fig. 11. Image of experiment result (a) Original Image (b) MSER Result (c) MSER+LBP Result (d) Proposed method

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Fig. 12. Image of experiment result (a) Original Image (b) MSER Result (c) MSER+LBP Result (d) Proposed method

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Fig. 13. Image of experiment result (a) Original Image (b) MSER Result (c) MSER+LBP Result (d) Proposed method

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Fig. 14. Image of experiment result (a) Original Image (b) MSER Result (c) MSER+LBP Result (d) Proposed method

Table. 1 Result of Character Detection Rate

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References

  1. X. C. Yin. & X. Yin. & K. Huang. & H. W. Hao. (2013). Robust Text Detection in Natural Scene Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(5), 970-983. Dol: 10.1109/TPAMI.2013.182
  2. Lele Xie, Tasweer Ahmad & Lianwen Jin. (2018). A New CNN-Based Method for Multi-Directional Car License Plate Detection, IEEE Transactions on Intelligent Transportation Systems, 19(2). 507-517. DOI: 10.1109/TITS.2017.2784093
  3. 1st Author Zhandong Liu et al. (2017). Method for unconstrained text detection in natural sence image. IET Computer Vision 11(7), 596-604 DOI: 10.1049/iet-cvi.20160452
  4. A. Raza & I. Siddiqi & C. Djeddi & A. Ennaji. (2013). Multilingual Artificial Text Detection Using a Cascade of Transforms. In Document Analysis and Recognition(ICDAR) 2013 12th International Conference on, 309-313.
  5. H. I. Koo & D. H. Kim. (2013). Scene Text detection via Connected Component Clustering and Nontext Filtering. IEEE transactions on image processing, 2296-2305.
  6. S. K. Pyo & G. S. Lee & Y. S. Park & S. H. Lee (2018). A license plate detection method based on contour extraction that adapts to environmental changes. Journal of the Korea Convergence Society, 9(9), 31-39. https://doi.org/10.15207/JKCS.2018.9.9.031
  7. Yuanyuan Feng, Yonghong Song. (2016). Yuanlin Zhang, Scene text detection based on multi-scale swt and edge filtering. 2016 23rd International Conference on, 645-650.
  8. P. Sujatha & K. K. Sudha. (2015). Performance Analysis of Different Edge Detection Techniques for Image Segmentation. Indian Journal of Science and Technology, 1-6.
  9. J. Y. Kim. (2015). Method on Detection Specific Region Using License Plate Edge Feature In Car Image. Master dissertation. Kwangwoon University, Seoul.
  10. C. Yu. & Y. h. Song. & Q. Meng. & Y. Zhang. & Y. Liu. (2015). Text detection and recongnition in natural scene with edge analysis. IET Computer Vision., 9(4) 603-613. DOI: 10.1049/iet-cvi.2013.0307.
  11. G. O. Kim & G. S. Lee & S. H. Lee (2014). An Edge Extraction Method Using K-means Clustering In Image. Journal of Digital Convergence. 12(11), 281-288. https://doi.org/10.14400/JDC.2014.12.11.281
  12. E. G. Rhee (2017). Security Algorithm for Vehicle Type Recognition. Journal of Convergence for Information Technology, 7(2), 77-82. https://doi.org/10.22156/CS4SMB.2017.7.2.077
  13. J. C. Han & B. C. Koo & K. J. Cheoi (2017). Obstacle Detection and Recognition System for Autonomous Driving Vehicle. Journal of Convergence for Information Technology, 7(6), 229-235. https://doi.org/10.22156/CS4SMB.2017.7.6.229
  14. L. Kang & Y. Li & D. Doermann. (2014). Orientation Robust Text Line Detection in Natural Images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4023-4041.
  15. M. Donoser. & H. Bischof. (2006) Efficent Maximally Stable Extermal Region (MSER) Tracking. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06). DOI: 10.1109/CVPR.2006.107
  16. M. W. Kim. & I. S. Oh. (2014) Stroke Width Transfrom and Feature Extraction for Maximally Stable Extermal Regions(MSER). Journal of KIISE : Computing Practices and Letters. 20(1), 21-25.
  17. A. Tabassum & S. A. Dhondse. (2015). Text Detection Using MSER and Stroke Width Transform. 2015 Fifth International Conference on Communication Systems and Network Technologies, 568-571. DOI: 10.1109/CSNT.2015.154
  18. J. H. Yang. (2017). Text area detection using text shape analysis in natural since image with enhanced object boundary. Master dissertation. Kwangwoon University, Seoul.
  19. M. K. Oh & J. C. Park (2017). Long distance vehicle license plate region detection using Low resolution feature of license plate region in road view images. Journal of Digital Convergence. 15(1), 239-245. https://doi.org/10.14400/JDC.2017.15.1.239
  20. A. Satpathy. & X. Jiang. & H. L. Eng. (2014) LBP-Based Edge-Texture Features for Object Recognition. IEEE Transactions on image Processing 23(5). DOI: 10.1109/TIP.2014.2310123
  21. K. Meena. & A. Suruliandi. (2011). Local binary patterns and its variants for face recognition. 2011 International Conference on Recent Trends in Information Technology (ICRTIT). 782-786.