Braille Recognition for Reducing Asymmetric Communication between the Blind and Non-Blind

Abstract

Assistive braille technology has existed for many years with the purpose of aiding the blind in performing common tasks such as reading, writing, and communicating with others. Such technologies are aimed towards helping those who are visually impaired to better adapt to the visual world. However, an obvious gap exists in current technology when it comes to symmetric two-way communication between the blind and non-blind, as little technology allows non-blind individuals to understand the braille system. This research presents a novel approach to convert images of braille into English text by employing a convolutional neural network (CNN) model and a ratio character segmentation algorithm (RCSA). Further, a new dataset was constructed, containing a total of 26,724 labeled braille images, which consists of 37 braille symbols that correspond to 71 different English characters, including the alphabet, punctuation, and numbers. The performance of the CNN model yielded a prediction accuracy of 98.73% on the test set. The functionality performance of this artificial intelligence (AI) based recognition system could be tested through accessible user interfaces in the future.

Keywords

• asymmetric communication
• optical braille recognition (OBR)
• artificial intelligence (AI)
• machine learning
• convolutional neural network (CNN)