Translation of Six Tuple Grade-1 Braille Alphabet to English Alphabet 
Vishwanath Venkatesh Murthy1, M Hanumanthappa2
1Vishwanath Venkatesh Murthy*, Assistant Professor, RNS Institute of Technology, Research scholar, Department of Computer Science and Applications, Bangalore University, Bengaluru, India.
2M Hanumanthappa, Department of Computer Science and Applications, Bangalore University, Bengaluru, India.
Manuscript received on June 20, 2021. | Revised Manuscript received on June 30, 2021. | Manuscript published on July 30, 2021. | PP: 8-11 | Volume-10, Issue-9, July 2021 | Retrieval Number: 100.1/ijitee.I92730710921 | DOI: 10.35940/ijitee.I9273.0710921
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Braille is the language used by visually impaired persons. Braille language comprises of collection of Braille cells which are embossed on a metal plate. Maintaining these bulky metal plates and distributing them to other parts of the world is a challenging task. This paper proposes a new technique of translating the Braille Cells embossed on plate to a natural language English character which can be easily distributed over network to make it globally accessible. Initially Braille documents are scanned, and preprocessing techniques like adaptive histogram and Laplacian filters are applied to augment the dots by eliminating the noise. The existence of dot pattern in every cell is detected with a Threshold and transformed to sequence of Binary matrix. A cell information is translated to 3×2 matrix with binary values of 0’s and 1’s representing absence and presence of dots in a cell. Convolutional Neural Network is used for feature extraction and Classification and Regression Trees (CART) classifier is utilized for recognize the character.
Keywords: Adaptive histogram, Braille, CART, CNN, CNART, Dilation, erosion, grade-1, segmentation, Threshold.