JACIII Vol.19 No.4 pp. 544-554
doi: 10.20965/jaciii.2015.p0544


Mathematical Morphology Based Image Segmentation and Character String Extraction Using Fuzzy Inference

Jianjun Chen and Noboru Takagi

Department of Intelligent Systems Design Engineering, Toyama Prefectural University
Imizu-shi, Toyama 939-0398, Japan

December 22, 2014
June 9, 2015
July 20, 2015
fuzzy inference, homogeneous region, natural scene image, text extraction, visually impaired people
Signs are ubiquitous indoors and outdoors, and they are often used for finding public places and other locations. However, information on signs is inaccessible to many visually impaired people, unless represented non-visually such as with Braille, tactile graphics, or speech. Automatically reading text from signs in natural scene images is a vital application for assisting visually impaired people. However, finding text in scene images is a great challenge because it cannot be assumed that the acquired image contains only characters. Natural scene images usually contain diverse text in different sizes, styles, fonts, and colors, and complex backgrounds. Therefore, we turn to the development of a portable camera-based assistive system to aid visually impaired people reading text from natural scenery. In this paper, a new method for character string extraction from scene images is discussed. The algorithm is implemented and evaluated using a set of natural scene images. Accuracy, precision, and recall rates of the proposed method are calculated and analyzed to determine success and limitations. Recommendations for improvements are given based on the results.
Cite this article as:
J. Chen and N. Takagi, “Mathematical Morphology Based Image Segmentation and Character String Extraction Using Fuzzy Inference,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.4, pp. 544-554, 2015.
Data files:
  1. [1] D. Pascolini and S. P. Mariotti, “Global Estimates of Visual Impairment: 2010,” British Journal Ophthalmology, Vol.96, No.5, pp. 614-618, 2012.
  2. [2]
  3. [3] J. Ishikawa and Y. Hyodo, “Developing an Accessible GPS System for the Blind,” Technical Report of IEICE, Vol.104, No.554, pp. 51-56, 2005.
  4. [4]
  5. [5] A. M. Ladd, K. E. Bekris, A. P. Rudys, D. S. Wallach, and L. E. Kavraki, “On the Feasibility of Using Wireless Ethernet for Indoor Localization,” IEEE Trans. on Robotics and Automation, Vol.20, No.3, pp. 555-559, 2004.
  6. [6] N. Tanaka and M. Okudaira, “A Study on Image Processing for Action Assistance of Visually Impaired Humans Using a Camera Equipped Cellular Phone,” Technical Report of the Institute of Image Information and Television Engineers, Vol.33, No.6, pp. 173-176, 2009.
  7. [7] N. Ezaki, K. Kiyota, B. T. Minh, M. Bulacu, and L. Schomaker, “Improved Text-Detection Methods for a Camera-based Text Reading System for Blind Persons,” Proc. of the 8th Int. Conf. on Document Analysis and Recognition, pp. 257-261, 2005.
  8. [8] S. M. Hanif and L. Prevost, “Texture Based Text Detection in Natural Scene Images: A Help to Blind and Visually Impaired Persons,” Proc. of Conf. & Workshop on Assistive Technologies for People with Vision & Hearing Impairments Assistive Technology for All Ages, 2007.
  9. [9] M. Pazio, M. Niedzwiecki, R. Kowalik, and J. Lebiedz, “Text Detection System for The Blind,” Proc. of the 15th European Signal Processing Conf., pp. 272-276, 2007.
  10. [10] Kalai Selvi U and Anish Kumar J, “Camera based Assistive Text Reading System using Gradient and Stroke Orientation for Blind Person,” Int. J. of Latest Trends in Engineering and Technology(IJLTET), Vol.4, Issue 1, pp. 325-330, 2014.
  11. [11] O. M. Foong and N. S. B. M. Razali, “Signage Recognition Framework for Visually Impaired People,” Proc. of Int. Conf. on Computer Communication and Management, pp. 488-492, 2011.
  12. [12] C. Yi, Y. Tian, and A. Arditi, “Portable Camera-based Assistive Text and Product Label Reading from Hand-held Objects for Blind Persons,” IEEE/ASME Trans. on Mechatronics, 2014.
  13. [13] Y. Matsuda, S. Omachi and H. Aso, “String Detection from Scene Images by Binarization and Edge Detection,” IEICE, D, Vol.J93-D, No.3, pp. 336-344, 2010.
  14. [14] H. Hase, M. Yoneda, M. Sakai, and H. Maruyama, “Consideration of Color Segmentation to Extract Character Areas from Color Document Images,” IEICE, D-II, Vol.J83-D-II, No.5, pp. 1294-1304, 2000.
  15. [15] K. Ashida, H. Nagai, M. Okamoto, H. Miyao, and H. Yamamoto, “Extraction of Characters from Scene Images,” IEICE, D-II, Vol.J88-D-II, No.9, pp. 1817-1824, 2005.
  16. [16] Y. Liu, T. Yamamura, N. Ohnishi, and N. Sugie, “Extraction of Character String Regions from a Scene Image,” IEICE, D-II, Vol.J81-D-II, No.4, pp. 641-650, 1998.
  17. [17] S. Saitoh, H. Goto, and H. Kobayashi, “Analysis and Comparison of Frequency Features for Scene Text Detection,” Technical Report of IEICE, PRMU2004-128, pp. 31-36, 2004.
  18. [18] D. Crandall, S. Antani, and R. Kasturi, “Extraction of Special Effects Caption Text Events from Digital Video,” Int. J. on Document Analysis and Recognition, Vol.5, pp. 138-157, 2003.
  19. [19] M. Inami, “A Character String Extraction Method from Scene Images Using Edge Detection and Color Clustering,” Master Thesis of Intelligent Systems Design Engineering, Toyama Prefectural University, Japan, 2012.
  20. [20] M. Gabbouj, P. Haavisto, and Y. Neuvo, “Recent Advances in Median Filtering,” Comunication, Control and Signal Processing, Vol.II, pp. 1080-1094, 1990.
  21. [21] J. Serra, “Toggle mappings,” From Pixels to Features, Elsevier, pp. 61-72, 1989.
  22. [22] L. B. Dorini and N. J. Leite, “A Scale-space Toggle Operator for Morphological Segmentation,” Proc. of the 8th Int. Symposium on Mathematical Morphology, Vol.1, pp. 101-112, 2007.
  23. [23] J. Fabrizio, B. Marcotegui and M. Cord. “Text Segmentation in Natural Scenes Using Toggle-Mapping,” Proc. of the 16th IEEE Int. Conf. on Image Processing, pp. 2349-2352, 2009.
  24. [24] J. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.PAMI-8, No.6, pp. 679-698, 1986.
  25. [25] R. O. Duda, P. E. Hart, and D. G. Stork, “Pattern Classification (2nd ed.),” A Wiley-Interscience Publication, 2001.

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