Conversion of Hindi Braille to Speech using Image and Speech Processing

This paper explores the conversion of Devanagari Hindi Braille, first to text, and subsequently to speech. The first part of the implementation is the conversion of Hindi Braille to text, in which two approaches are used for Braille character recognition: a conventional sequence-mapping approach and a deep learning-based method. The second part of the paper deals with the conversion of Hindi text to speech, in which text is converted to speech by concatenating speech samples corresponding to Hindi vowels and consonants. Successful conversion of Hindi Braille to text and, consequently, speech, yielded two forms of output. Generated samples of Hindi Braille letters, as well as extracts from a Hindi Braille textbook, were used to create an image dataset. A Hindi speech corpus was created using speech coefficients extracted from a recorded audio sample. The authors achieved an accuracy of 100 percent using the conventional method of Hindi Braille to text conversion and an accuracy of 96 percent using the deep learning approach. Experts also validated the quality of Hindi speech generated from the text-to-speech model, based on factors such as clarity of speech, pronunciation, sound quality, and speed of speech.