logo

SCIENCE CHINA Information Sciences, Volume 59 , Issue 7 : 072101(2016) https://doi.org/10.1007/s11432-016-5575-z

A method for automatically translating print books into electronic Braille books

More info
  • ReceivedNov 10, 2015
  • AcceptedDec 29, 2015
  • PublishedJun 16, 2016

Abstract


Acknowledgment

Acknowledgments

The present research was conducted by the research fund of Dankook University in 2013.


References

[1] Improvement suggestion for ensuring accessibility to knowledge and information of disabled people. ACRC Technical Report. Korea, 2014. Google Scholar

[2] Wong E K, Chen M. A new robust algorithm for video text extraction. Patt Recogn, 2003, 36: 1397-1406 CrossRef Google Scholar

[3] Shivakumara P, Phan T Q, Tan C L. A gradient difference based technique for video text detection. In: Proceedings of the 10th International Conference on Document Analysis and Recognition, Barcelona, 2009. 156--160. Google Scholar

[4] Jang D G, Hwang C S. Document image layout analysis using image filters and constrained conditions. KIPS Trans B, 2002, 9: 311-318 Google Scholar

[5] Chun B T, Bae Y I, Kim T Y. Text extraction in videos using topographical feature of characters. In: Proceedings of the IEEE International Conference on Fuzzy System, Seoul, 1999. 1126--1130. Google Scholar

[6] Fu X L, Cai L H, Liu Y, et al. A computational cognition model of perception, memory, and judgment. Sci China Inf Sci, 2014, 57: 032114-318 Google Scholar

[7] Liu Y, Fu Q, Liu Y, et al. A distributed computational cognitive model for object recognition. Sci China Inf Sci, 2013, 56: 092101-318 Google Scholar

[8] Strouthopoulos C, Papamarkos N, Atsalakis A E. Text extraction in complex color document. Patt Recogn, 2002, 35: 1743-1758 CrossRef Google Scholar

[9] Chun B T, Song C Y. A method for character segmentation using frequency characteristics and back propagation neural network. J Korea Soc Comput Inf, 2006, 4: 55-60 Google Scholar

[10] Yuan Q, Tan C L. Text extraction from gray scale document images using edge information. In: Proceedings of the 6th International Conference on Document Analysis and Recognition, Seattle, 2001. 302--306. Google Scholar

[11] Park J C. Text region detection of various slope and size of text using the adaptive character-edge map. In: Proceedings of KOCON Conference, 2007. 5--9. Google Scholar

[12] Grover S, Arora K, Mitra S K. Text extraction from document images using edge information. In: Proceedings of the IEEE India Council Conference, Gujarat, 2009. 1--4. Google Scholar

[13] Kim E J. Character area extraction and the character segmentation on the color document. J Korean Inst Intell Syst, 1999, 9: 444-450 Google Scholar

[14] Song Y J, Kim K C, Choi Y W, et al. Text region extraction and text segmentation on camera-captured document style images. In: Proceedings of the 8th International Conference on Document Analysis and Recognition, Seoul, 2005. 172--176. Google Scholar

[15] Kim J S, Kim S H. Three-level color clustering algorithm for binarizing scene text images. KIPS Trans B, 2005, 12: 1-8 Google Scholar

[16] Jung K C, Han J H. Hybrid approach to efficient text extraction in complex color images. Patt Recogn Lett, 2004, 25: 679-699 CrossRef Google Scholar

[17] Jung K C. Hybrid approach of texture and connected component methods for text extraction in complex images. IEEK, 2004, 41: 175-186 Google Scholar

[18] Huang T J, Tian Y H, Li J, et al. Salient region detection and segmentation for general object recognition and image understanding. Sci China Inf Sci, 2011, 54: 2461-2470 CrossRef Google Scholar

[19] Zhou L, Hu D W, Zhou Z T. Scene recognition combining structural and textural features. Sci China Inf Sci, 2013, 56: 078106-2470 Google Scholar

[20] Smith R. An overview of the Tesseract OCR Engine. In: Proceedings of the 12th International Conference on Document Analysis and Recognition. Washington DC: IEEE, 2007. 629--633. Google Scholar

[21] Ministry of Culture, Sports and Tourism. Korea Braille Regulations, MCST Regulation 2006-39. Korea, 2006. Google Scholar

[22] Way T P, Barner K E. Automatic visual to tactile translation-part II: evaluation of the TACTile image creation system. IEEE Trans Rehabil Eng, 1997, 5: 95-105 CrossRef Google Scholar

[23] Rotard M, Knödler S, Ertl T. A tactile web browser for the visually disabled. In: Proceedings of the 16th ACM Conference on Hypertext and Hypermedia. New York: ACM, 2005. 15--22. Google Scholar

[24] Ladner R E, Ivory M Y, Rao R, et al. Automating tactile graphics translation. In: Proceedings of the 7th International ACM SIGACCESS Conference on Computers and Accessibility. New York: ACM, 2005. 150--157. Google Scholar

[25] Petit G, Dufresne A, Levesque V, et al. Refreshable tactile graphics applied to schoolbook illustrations for students with visual impairment. In: Proceedings of the 10th International ACM SIGACCESS Conference on Computers and Accessibility. New York: ACM, 2008. 89--96. Google Scholar

[26] Chen J J, Nagaya R, Takagi N. An extraction method of solid line graph elements in mathematical graphs for automating translation of tactile graphics. In: Proceedings of the 13th IEEE International Symposium on Advanced Intelligent Systems. Washington DC: IEEE, 2012. 422--427. Google Scholar

[27] Takagi N, Chen J J. A broken line classification method of mathematical graphs for automating translation into scalable vector graphic. In: Proceedings of the IEEE 43rd International Symposium on Multiple-Valued Logic. Washington DC: IEEE, 2013. 71--76. Google Scholar

[28] Chen J J, Takagi N. A pattern recognition method for automating tactile graphics translation from hand-drawn maps. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. Washington DC: IEEE, 2013, 4173--4178. Google Scholar

[29] Chen Q, Zhao L, Lu J, et al. Modified two-dimensional Otsu image segmentation algorithm and fast realization. IET Image Process, 2012, 6: 426-433 CrossRef Google Scholar

[30] Deng Y N, Kenney C, Moore M S, et al. Peer group filtering and perceptual color image quantization. In: Proceedings of the IEEE International Symposium on Circuits and Systems. Washington DC: IEEE, 1999. 21--24. Google Scholar

[31] Gong Y. Advancing content-based image retrieval by exploiting image color and region features. Multimedia Syst, 1999, 7: 449-457 CrossRef Google Scholar

[32] Shi J, Tomasi C. Good features to track. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Washington DC: IEEE, 1994. 593--600. Google Scholar

qqqq

Contact and support