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Sinhala Braille Translator
| Content Provider | Semantic Scholar |
|---|---|
| Author | Silva, N. H. K. V. De Liyanage, Sidath Ravindra |
| Copyright Year | 2016 |
| Abstract | Braille is one of the most valuable and indispensable method used for written communication by the blind. Typically, editing and reprinting of Braille that are embossed on paper are done manually. It is a time consuming and labor intensive task. Motivation to understand Braille codes by sighted people has encouraged the development of Optical Braille Recognition (OBR) for different languages across the World. However, OBR for Sinhala Braille has not been attempted previously. A communication gap exists among the blind and the Braille illiterate sighted people in the Sri Lankan society as written communication is restricted. This paper addresses this communication gap between the blind and sighted people using the Sinhala language. The system is capable of extracting Braille characters from a Braille document followed by decoding them into Sinhala characters. The decoded Sinhala characters are further normalized to Sinhala text that is legible for a sighted person. The system is capable of recognizing an image of embossed Sinhala Braille and then converts it to Sinhala text. It takes the scanned braille documents using normal flat-bed scanner and those images undergoes pre-processing steps. Noise removal is done using gray scaling and the noise filtered image is converted to a threshold image. Then by applying edge detection techniques braille documents are subjected to segmentation. For each and every character, feature extraction is carried out identifying main dots. Then the extracted features are used to train the classifier to predict the Sinhala character. Artificial Neural Networks (ANN), Support Vector Machines (SVM) and KNearest Neighbour (K-NN) classifiers were tested on the sample Braille documents. Finally decoded characters are normalized to Sinhala text which is in human understandable form. The prototype Sinhala Braille Translator was tested on documents collected from Library, University of Kelaniya. The system was able to recognize Single sided Sinhala Braille with an average accuracy of 91.2%. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ijtrd.com/papers/IJTRD4058.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
