Study on the extraction of the text region from natural scene images by an analysis of the edge-oriented pattern

  • Authors

    • Jae Ho Yang
    • Gang Seong Lee
    • Young Pyo Hong
    • Sang Hun Lee
    2018-04-03
    https://doi.org/10.14419/ijet.v7i2.12.11029
  • Text Detection, Histogram Equalization, SWT Algorithm, Edge-Based Method, Texture-Based Method, Pattern Analysis.

  • Abstract

    Background/Objectives: In this paper, we propose a hybrid scene-detection method using an edge and textural analysis in natural scene images, and finally, we detect the text regions by removing the non-text regions through a pattern analysis of each region.

    Methods/Statistical analysis: The proposed algorithm is divided into the pre-processing stage and the extraction processing stage to perform the text detection. The lost texts that are improved through a histogram equalization for the minimization of the loss of the text parts that is due to light exposure are detected before the edge detection. After that, the edge is detected using the Canny operator. The detected edge is obtained in the step of applying the SWT algorithm to detect the text candidate regions. The extraction processing step is the step of removing the noise region that is detected by the pixel analysis of the SWT algorithm, and it analyzes the pattern of the text regions and then removes the non-text regions to finally detect the text regions. For the quantitative comparison of the proposed algorithm, our results are compared with the ground-truth image using the precision, recall, and F-measure.

    Findings: One of the existing text-detection algorithms, the edge-based method, is problematic, as, in addition to the text, the complex backgrounds and textures are detected as the edges in natural scene images. The connected component-based method is also problematic, as the non-text region is included in the text region in the process of finding the connection component.

    Improvements/Applications: The proposed method shows an effective text-detection result regardless of the light exposure in natural scene images compared with the conventional text-detection algorithm.

     

     

  • References

    1. [1] C.P.Sumathi, T.Santhanam, N.Priya, “Techniques and challenges of automatic text extraction in complex images: a surveyâ€, Journal of Theoretical and Applied Information Technology, Vol. 35, No. 2, 2012, pp. 225-235.

      [2] Raza A, Siddiqi I, Djeddi C, Ennaji A,"Multilingual artificial text detection Using a Cascade of Transforms" In Proceeding of 2013 12th International Conference on Document Analysis and Recognition (ICDAR), 2013, pp. 309–313.

      [3] Tahani Khatib, Huda Karajeh, Hiba Mohammad, Lama Rajab,“A hybrid multilevel text extraction algorithm in scene images" Scientific Research and Essays, 2015, pp.105-113.

      [4] C. Liu, C. Wang, R. Dai, "Text detection in images based on unsupervised classification of edge-based features", Proc. IEEE Int. Conf. Doc. Anal. Recognition, 2005, pp. 610-614.

      [5] G. S. Lee, J. H. Park, J. S. Kim, S. H. Ryu, S. H. Lee, “Independent Object Tracking from Video using the Contour Information in HSV Color Spaceâ€, Indian Journal of Science and Technology, 2016, pp.1-8.

      [6] Ji R, Xu p, Yao H, Zhang Z, Sun X, Liu T, “Directional Correlation Analysis of Local Haar Binary Pattern for Text Detection.†Proceeding of the International Conference on Multimedia and Expo, 2008, pp.885-888.

      [7] H. Koo, D. H. Kim, "Scene text detection via connected component clustering and non-text filtering", IEEE Trans. Image Process, 2013, pp. 2296-2305.

      [8] G. S. Lee, J. H. Park, S. H. Lee, “A Study on the Convergence Technique enhanced GrabCut algorithm using color histogram and modified sharpening filterâ€, Korea convergence society, 2015, pp. 1-8.

      [9] Y. Feng, Y, Song, Y, Zhang, “Scene Text Detection Based on Multi-Scale SWT and Edge Filteringâ€, Pattern Recognition (ICPR), 2016.

      [10] B. Epshtein, E. Oyek, and Y. Wexler. Detecting text in natural scenes with stroke width transform. In CVPR, 2010, pp.2963-2970.

      [11] Le Kang, Yi Li, David Doermann, “Orientation Robust Text Line Detection in Natural Imagesâ€, Vision and Pattern Recognition (CVPR), 2014, pp. 1-8.

      [12] Cong Yao, Xiang Bai, Wenyu Liu, Yi Ma, and Zhuowen Tu, “Detecting texts of arbitrary orientations in natural images,†Vision and Pattern Recognition(CVPR),2012pp. 1083–1090.

      [13] Boris Epshtein, Eyal Ofek, and Yonatan Wexler, “Detecting text in natural scenes with stroke width transform,†Vision and Pattern Recognition (CVPR), 2010, pp. 2963–2970.

      [14] Xiangrong Chen and Alan L Yuille, “Detecting and reading text in natural scenes,†Vision and Pattern Recognition (CVPR), 2004pp. 359–366.

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  • How to Cite

    Ho Yang, J., Seong Lee, G., Pyo Hong, Y., & Hun Lee, S. (2018). Study on the extraction of the text region from natural scene images by an analysis of the edge-oriented pattern. International Journal of Engineering & Technology, 7(2.12), 29-34. https://doi.org/10.14419/ijet.v7i2.12.11029

    Received date: 2018-04-03

    Accepted date: 2018-04-03

    Published date: 2018-04-03