Tactile graphics are images that use raised surfaces so that a visually impaired person can feel them. Tactile maps are used by blind and partially sighted people when navigating around an environment, and they are also used prior to a visit for orientation purposes. Since the ability to read tactile graphics deeply depends on individuals, providing tactile graphics individually is needed. This implies that producing tactile graphics should be as simple as possible. Based on this background, we are developing a system for automating production of tactile maps from hand-drawn figures. In this paper, we first present a pattern recognition method for hand-drawn maps. The usability of our system is then evaluated by comparing it with the two different methods to produce tactile graphics. 1. Introduction Producing tactile maps is an important effort to bring blind people to more self-supported life. There have been many studies of computer-aided systems in order to assist the production of tactile graphics [1–6]. Tactile map automated creation system (TMACS) [2, 3], for example, has been developed to produce tactile maps automatically. It is a web application and produces the digital file for a tactile map from the information about two places: departure place and destination. TMACS assumes that users can be blind, and so it produces the tactile map automatically from the map database if a user only provides a departure place and destination to the system. However, tactile maps produced by TMACS are sometimes difficult to read for the blind because it is possible to include unnecessary information in the tactile maps. Further, Geospatial Information Authority of Japan has also developed a tactile map production system [4]. This system assumes that users are sighted people, and it is totally provided as a GUI application. Operating this GUI application is not easy for users who are not familiar with computers. Based on the background above, we are now developing a system for automating production of tactile maps. In the tactile map production method using our system, a sighted user first draws manually a hand-drawn map using a pencil and paper, and the map is then converted to a digital image using an image scanner or a digital camera. Finally, by using our system, the digital image is recognized and translated into digital files which are available to produce the tactile maps. Our system chooses the Edel [7] and scalable vector graphics (SVG) [8] documents as the output file formats. Here, Edel is a software system to create digital files available to
References
[1]
J. A. Miele, S. Landau, and D. Gilden, “Talking TMAP: automated generation of audio-tactile maps using Smith-Kettlewell's TMAP software,” The British Journal of Visual Impairment, vol. 24, no. 2, pp. 93–100, 2006.
[2]
T. Watanabe, T. Yamaguchi, K. Watanabe, et al., “Development and evaluation of a tactile map automated creation system accessible to blind persons,” IEICE Transactions on Information and Systems, vol. J94-D, no. 10, pp. 1652–1663, 2011 (Japanese).
S. E. Krufka and K. E. Barner, “Automatic production of tactile graphics from scalable vector graphics,” in Proceedings of the 7th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '05), pp. 166–172, October 2005.
[6]
R. E. Ladner, M. Y. Ivory, R. Rao et al., “Automating tactile graphics translation,” in Proceedings of the 7th International ACM SIGACCESS Conference on Computer and Accessibility, pp. 150–157, New York, NY, USA, October 2005.
[7]
Edel, http://www7a.biglobe.ne.jp/ EDEL-plus.
[8]
J. D. Eisenberg, SVG Essentials, O'REILLY, 2002.
[9]
K. Broelemann, X. Jiang, and A. Schwering, “Automatic street graph construction in sketch maps,” in Graph-Based Representations in Pattern Recognition, X. Jiang, M. Ferrer, and A. Torsello, Eds., vol. 6658 of Lecture Notes in Computer Science, pp. 275–284, Springer, Berlin, Germany, 2011.
[10]
L. B. Kara and T. F. Stahovich, “An image-based, trainable symbol recognizer for hand-drawn sketches,” Computers & Graphics, vol. 29, no. 4, pp. 501–517, 2005.
[11]
B. Edwards and V. Chandran, “Machine recognition of hand-drawn circuit diagrams,” in Proceedings of the IEEE Interntional Conference on Acoustics, Speech, and Signal Processing, vol. 6, pp. 3618–3621, June 2000.
[12]
P. Sala, “A recognition system for symbols of electronic components in hand-written circuit diagrams,” CSC 2515-Machine Learning Project Report, 2004.
[13]
Y. Qiao and M. Yasuhara, “Recovering dynamic information from static handwritten images,” in Proceedings of the 9th International Workshop on Frontiers in Handwriting Recognition, pp. 118–123, jpn, October 2004.
[14]
M. Notowidigdo and R. C. Miller, “Off-line sketch interpretation,” in Proceedings of the AAAI Fall Symposium on Making Pen-Based Interaction Intelligent and Natural, pp. 120–126, Arlington, Va, USA, April 2004.
[15]
R. J. Gonzalez and R. E. Woods, Digital Image Processing, Pearson Education, Upper Saddle River, NJ, USA, 3rd edition, 2007.
[16]
N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979.
[17]
C. J. Hilditch, “Linear skeletons from square cupboards,” in Machine Intelligence, Vol. IV, B. Meltzer and D. Michie, Eds., pp. 403–420, Elsevier, New York, NY, USA, 1969.
[18]
S. Hermann and R. Klette, “Global curvature estimation for corner detection,” Communication and Information Technology Research Technical Report 171, 2005.
[19]
N. Nain, V. Laxmi, B. Bhadviya, and A. Gopal, “Corner detection using difference chain code as curvature,” in Proceedings of the 3rd IEEE International Conference on Signal Image Technologies and Internet Based Systems (SITIS '07), pp. 821–825, Shanghai, China, December 2007.
[20]
B. Kerautret, J. Lachaud, and B. Naegel, “Curvature based corner detector for discrete, noisy and multi-scale contours,” International Journal of Shape Modeling, vol. 14, no. 2, pp. 127–145, 2008.
[21]
K. Wall and P. Danielsson, “A fast sequential method for polygonal approximation of digitized curves,” Computer Vision, Graphics, & Image Processing, vol. 28, no. 2, pp. 220–227, 1984.
[22]
D. Chetverikov, “A simple and efficient algorithm for detection of high curvature points in planar curves,” in Computer Analysis of Images and Patterns, vol. 2756 of Lecture Notes in Computer Science, pp. 746–753, Springer, Berlin, Germany, 2003.
[23]
T. Terano, K. Asai, and M. Sugeno, Fuzzy Systems and Its Applications, Academic Press, Boston, Mass, USA, 2nd edition, 1992.
[24]
N. Ono and R. Takiyama, “On calculations of curvature of sampled curves,” Technical Report of IEICE IE93-74, 1993 (Japanese).
[25]
P. J. Schneider, “An algorithm for automatically fitting digitized curves,” in Graphics Gems, A. S. Glassner, Ed., pp. 612–625, Academic Press, 1990.
