A Tesseract-based Optical Character Recognition for a Text-to-Braille Code Conversion

Robert G. de Luna


This study provided a platform that converts printed text documents into corresponding braille code that will trigger the palpable output of the braille cells. The system is composed of two main parts: the image scanner and the microcontroller-based braille platform. The image scanner captures the printed text document and performs a series of pre-processing algorithms where the processed image will be subjected to character recognition using Tesseract. It is open-source software for character recognition capable of recognizing text characters in different fonts and sizes. SimpleCV, also an open-source software for computer vision and a simpler version of an OpenCV, was utilized in pre-processing of images where binarization, filtering, edge detection, and character segmentation are performed. This will allow the microcontroller-based braille platform to interpret the printed characters from the generated braille code in ASCII format that will trigger the palpable output of the braille cells. The developed system was subjected to functionality and accuracy testing to assess its performance. Accuracy was based on the capability of the system to produce the right braille outputs that match the scanned line of text which are in Arial font. The testing was conducted utilizing the Arial font size of 12, 14, 16, 18, 20, 22, and 24. Results show that the system is capable of recognizing text with greater than 85 % accuracy starting at font size 18 with an average accuracy of 88.09 % and increases accordingly as the font size increases.


optical character recognition; braille; image processing; tesseract; text-to-braille.

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M. Jaymalin, “Over 2 million Pinoys blind, sight-impaired,” The Philippine Star, 6 August 2017. [Online]. Available: https://www.philstar.com/headlines/2017/08/06/1726085/over-2- million-pinoys-blind-sight-impaired. [Accessed 2018].

S. S. d. Guzman, “The 'blind' side,” The Philippine Star, 4 July 2011. [Online]. Available: https://www.philstar.com/opinion/2011/07/04/702093/blind-side. [Accessed 2018].

C. Simpson, “Rules of Unified English Braille,” 2013. [Online]. Available: http://iceb.org/ueb.html. [Accessed 2018].

R. Sarkar, S. Das and D. Rudrapal, “A low cost microelectromechanical Braille for blind people to communicate with blind or deaf-blind people through SMS subsystem,” in 3rd IEEE International Advance Computing Conference (IACC)., Ghaziabad, 2013.

P. Rajarapollu, S. Kodolikar, D. Laghate and A. Khavale, “FPGA Based Braille to Text & Speech for Blind Persons,” International Journal of Scientific & Engineering Research, vol. 4, no. 4, pp. 348-353, 2013.

A. K. Garg, “The unified braille Unicode system: Presenting an ideal unified system around 8-dot Braille Unicode for the braille users world-over,” in 2016 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), Bangalore, 2016.

M. E. Adnan, N. M. Dastagir, J. Jabin, A. M. Chowdhury and M. R. Islam, “A cost effective electronic braille for visually impaired individuals,” in 2017 IEEE Region 10 Humanitarian Technology Conference, 2017, Dhaka.

A. Moise, G. Bucur and C. Popescu, “Automatic System for Text to Braille Conversion,” in 2017 9th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Targoviste, 2017.

S. Sultana, A. Rahman, F. H. Chowdhury and H. U. Zaman, “A novel Braille pad with dual text-to-Braille and Braille-to-text capabilities with an integrated LCD display,” in 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kannur, 2017.

B. K. Rajan and V. Anjitha, “Braille code conversion to voice in malayalam,” in 2017 International Conference on Communication and Signal Processing (ICCSP), Chennai, 2017.

M. A. Rahman, K. Dhar and M. A. Ullah, “Hardware design and implementation of Bengali Braille embosser,” in 2017 4th International Conference on Advances in Electrical Engineering (ICAEE), Dhaka, 2017.

A. Raghunandan and A. MR, “The Methods Used in Text to Braille Conversion and Vice Versa,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 5, no. 3, 2017.

P. De Silva and N. Wedasinghe, “Braille Converter and Text-To-Speech Translator for Visually Impaired People in Sri Lanka,” American Journal of Mobile Systems, Applications and Services, vol. 3, no. 1, pp. 1-9, 2017.

M. W. Haas, “This Device Translates Text to Braille in Real Time,” Smithsonian, 2017. [Online]. Available: https://www.smithsonianmag.com/innovation/device-translates-text-braille-real-time-180963171/.

J. Marot and S. Bourennane, “Raspberry Pi for image processing education,” in 25th European Signal Processing Conference (EUSIPCO), Greece, 2017.

“Logitech,” 2018. [Online]. Available: https://www.logitech.com/en-us/video/webcams. [Accessed 2018].

“Computer Vision platform using Python,” SimpleCV, 2018. [Online]. Available: http://www.simplecv.org.

“Getting Started with MATLAB Support Package for Raspberry Pi Hardware,” Mathworks, 2018. [Online]. Available: https://au.mathworks.com/help/supportpkg/raspberrypiio/examples/getting-started-with-matlab-support-package-for-raspberry-pi-hardware.html.

“Tesseract OCR,” Tesseract, 2018. [Online]. Available: https://github.com/tesseract-ocr/tesseract.

“Mega 2560 R3 Board based on Arduino,” MakerLab Electronics, 2018. [Online]. Available: https://www.makerlab-electronics.com/product/arduino-mega-2560-r3/.

DOI: http://dx.doi.org/10.18517/ijaseit.10.1.9956


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