Machine Learning Based Braille Transliteration of Odia Language
Vinod Jha1, K. Parvathi2

1Vinod Jha*, Assistant Professor, Department of Electronics and Telecommunication, KIIT, India.
2Dr. K. Parvathi, Administrator, Department of Electronics and Telecommunication, Andhra University, Visakhapatnam, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 23, 2020. | Manuscript published on March 10, 2020. | PP: 1866-1871 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2820039520/2020©BEIESP | DOI: 10.35940/ijitee.E2820.039520
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Abstract: Braille transliteration of natural languages is required for providing a better opportunity of learning and creating opportunities of eceity people. It allows a bigger diaspora of non-blind teachers to have written communication with blind people. The present paper proposes a method of Braille transliteration of Handwritten and printed Odia characters automatically into Braille. The current work proposes a method of Braille transliteration of Handwritten Odia text with industry applicable accuracy. The method first preprocesses the text and then segments it into characters and then uses an SVM classifier trained on HOG features of Odia handwritten characters to predict characters and maps the predicted printable character to its corresponding Braille with a very good accuracy. The method can further be used with text to speech engines to help the blind students use this technique with refreshable Braille having audio facility to listen the same. 
Keywords: Braille, Optical Character Recognition, HOG, SVM, Unicode
Scope of the Article: Machine Learning