Braille Recognition by E-skin System based on Binary Memristive neural network

Abstract

Abstract Braille system is world widely used for visually impaired people for communication. However, there are still some visually impaired people who are not able to learn Braille system due to various factors, such as the age (too young or too old), damage of brain, etc. A wearable and low-cost Braille recognition system may substantially help these people recognize Braille or assist them in Braille learning. In this work, we fabricated polydimethylsiloxane (PDMS)-based flexible pressure sensors to construct an electronic skin (E-skin) for the application of Braille recognition. The E-skin mimics the human touch sensing function for collecting Braille information. The Braille recognition is realized with a neural network based on memristors. We utilize a binary neural network algorithm with only two bias layers and three fully connected layers. Such neural network design remarkably reduces the calculation burden and thus the system cost. Experiments show that the system can achieve a recognition accuracy of up to 91.25%. This work demonstrates the possibility to realize a wearable and low-cost Braille recognition system and a Braille learning-assistance system.