Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

Impact Factor

2.10

CiteScore

Kuo-Ming Hung This email address is being protected from spambots. You need JavaScript enabled to view it.1, Chi-Hsiao Yih2 and Cheng-Hsiang Yeh2

1Department of Information Management, Kainan University, Taoyuan, Taiwan 338, R.O.C
2Department of Electrical and Computer Engineering, Tamkang University, Tamsui, Taiwan 251, R.O.C


 

Received: December 25, 2017
Accepted: January 18, 2018
Publication Date: September 1, 2018

Download Citation: ||https://doi.org/10.6180/jase.201809_21(3).0019  

ABSTRACT


A novel reading assistant system for blind, visually impaired people or children is developed in this paper. The user can choose the region to be read and then receive output via text to speech. We design a helpful system that users can choose the area (on the book) they want to listen when reading and the device can also speak out the entire article. Besides, users can read without distance perception by using this system. The step to construct this system is according to the following: first, this system is used depth images to detect the coordinates of the user’s finger with Kinect, and the book is also input with Kinect. Next, we utilize text preprocessing to remove the border of the book and then take advantage of the common attribute of text height to eliminate images and tables. Beside, we also use a method to combine non-linear and linear compensation for correcting distortions of document images. Experimental results demonstrate that the proposed scheme has good performance and demonstrates robustness. It also provides an effective interactive platform.


Keywords: Assistant System, Restoring Warped, Text Recognition, Kinect, Depth Image


REFERENCES


  1. [1] WHOReport:Visualimpairmentandblindness(2014).
  2. [2] Araki, M., Shibahara, K. and Mizukami, Y., “Spoken Dialogue System for Learning Braille,” 35th IEEE Annual Computer Software and Applications Conference, pp. 152156 (2011). doi: 10.1109/COMPSAC. 2011.27
  3. [3] Arshad, H., Khan, U. S. and Izhar, U., “MEMS Based Braille System,” 2015 IEEE 15th International Conference on Nanotechnology, pp. 11031106 (2015). doi: 10.1109/NANO.2015.7388815
  4. [4] Matsuda, Y., Isomura, T., Sakuma, I., Kobayashi, E., Jimbo, Y. and Arafune, T., “Finger Braille Teaching System for People who Communicate with Deafblind People,” Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, pp. 32023207 (2007). doi: 10.1109/ICMA.2007.4304074
  5. [5] Al-Shamma, S. D. and Fathi, S., “Arabic Braille Recognition and Transcription into Text and Voice,” 5th Cairo International Biomedical Engineering Conference Cairo, pp. 1618 (2010). doi: 10.1109/CIBEC. 2010.5716095
  6. [6] Abdul Malik, S. A. S., Ali, E. Z., Yousef, A. S., Khaled, A. H. and Abdul Aziz, O. A. Q., “An Efficient Braille Cells Recognition,” 6th International Conference on Wireless Communications Networking and Mobile Computing (WICOM), pp. 14 (2010). doi: 10.1109/ WICOM.2010.5601020
  7. [7] Onur, K., “Braille-2 Otomatik Yorumlama Sistemi,” Signal Processing and Communications Applications Conference (SIU), pp. 15621565 (2015). doi: 10. 1109/SIU.2015.7130146
  8. [8] Velazquez, R., Preza, E. and Hernandez, H., “Making eBooks Accessible to Blind Braille Readers,”IEEE International Workshop on Haptic Audio Visual Environments and Their Applications, pp. 2529 (2008). doi: 10.1109/HAVE.2008.4685293
  9. [9] Rantala, J., Raisamo, R., Lylykangas, J., Surakka, V., Raisamo, J., Salminen, K., Pakkanen, T. and Hippula, A., “Methods for Presenting Braille Characters on a Mobile Device with a Touchscreen and Tactile Feedback,” IEEE Transactions on Haptics, Vol. 2, No. 1, pp. 2839 (2009). doi: 10.1109/TOH.2009.3
  10. [10] Gaudissart, V., Ferreira, S., Thillou, C. and Gosselin, B., “Mobile Reading Assistant for Blind People,” Proceedings of European Signal Processing Conference, pp. 538544 (2005).
  11. [11] Neto, R. and Fonseca, N., “Camera Reading for Blind People,” Procedia Technology, Vol. 16, pp. 12001209 (2014). doi: 10.1016/j.protcy.2014.10.135
  12. [12] http://www.u-tran.com/index.php,visitedinMay(2016).
  13. [13] Noguchi, S. and Yamada, M., “Real-time 3D Page Tracking and Book Status Recognition for High-speed Book Digitization Based on Adaptive Capturing,” 2014 IEEEWinterConference on ApplicationsofComputer Vision (WACV), pp. 2426 (2014).
  14. [14] Stamatopoulos, N., Gatos, B. and Kesidis, A., “Automatic Borders Detection of Camera Document Images,” Int. Workshop Camera-BasedDocument Anal. Recognition Conf. (CDBAR), pp. 7178 (2007).
  15. [15] Gatos, B., Pratikakis, I. and Perantonis, S. J., “Adaptive Degraded Document Image Binarization,” Pattern Recognition, Vol. 39, pp. 317327 (2006). doi: 10. 1016/j.patcog.2005.09.010
  16. [16] Otsu, N., “A Threshold Selection Method from Graylevel Histogram,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, pp. 6266 (1976). doi: 10. 1109/TSMC.1979.4310076
  17. [17] Wahl, F. M., Wong, K. Y. and Casey, R. G., “Block Segmentation and Text Extraction in Mixed Text/Image Documents”, Computer Graphics and Image Processing, Vol. 20, pp. 375390 (1982). doi: 10.1016/ 0146-664X(82)90059-4
  18. [18] Chang, F., Chen, C. J. and Lu, C. J., “A Linear-time Component-labeling Algorithm Using Contour Tracing Technique,” Computer Vision and Image Understanding, Vol. 93, No. 2, pp. 206220 (2004). doi: 10. 1016/j.cviu.2003.09.002
  19. [19] Jiang, H., “Research on the Document Image Segmentation Based on the LDA Model,” Advances in Information Sciences and Service Sciences (AISS), Vol. 4, No. 3, pp. 1218 (2012). doi: 10.4156/aiss.vol4.issue3.2
  20. [20] Xiaoying, Z., “Study on Document Image Segmentation Techniques Based on Improved Partial Differential Equations,” Journal of Convergence Information Technology (JCIT), Vol. 8, No. 5, pp. 821831 (2013). doi: 10.4156/jcit.vol8.issue5.96
  21. [21] Stamatopoulos, N., Gatos, B., Pratikakis, I. and Perantonis, S. J., “Goal-oriented Rectification of Camerabased Document Images,” IEEE Trans. on Image Processing, Vol. 20, No. 4, pp. 910920 (2011). doi: 10. 1109/TIP.2010.2080280
  22. [22] Gao, Y., Ai, X., Rarity, J. and Dahnoun, N., “Obstacle Detection with 3D CameraUsing U-VDisparity,” Proceedings of the 2011 7th International Workshop on Systems, Signal Processing and Their Applications (WOSSPA), pp. 239–242 (2011). doi: 10.1109/WOSSPA.2011.5931462
  23. [23] Huang, H. C., Hsieh, C. T. and Yeh, C. H., “An Indoor Obstacle Detection System Using Depth Information,” Sensors (Basel), Vol. 15, No. 10, pp. 2711627141 (2015). doi: 10.3390/s151027116
  24. [24] Hsieh, C. T., Lee, S. C. and Yeh, C. H., “Restoring Warped Document Image Based on Text Line Correction,” 2013 9th International Conference on Computing Technology and Information Management (ICCM 2013), Vol. 14, pp. 459464 (2013).
  25. [25] Hsieh, C. T., Yeh, C. H., Liu, T. T. and Huang, K. C., “Non-visual Document Recognition for Blind Reading Assistant System,” 2013 9th International Conference on ComputingTechnology and Information Management (ICCM 2013), Vol. 14, pp. 453458 (2013).
  26. [26] Tesseract: https://github.com/tesseract-ocr, visited in May (2016).
  27. [27] Text-to-speech:http://msdn.microsoft.com/en-us/library/ ms720163.aspx, visited in May (2016).