Open AccessBraille character detection using deep neural networks for an educational robot for visually impaired people
Author(s) -
Diego Bertolini,
Gabriel N. P. dos Santos,
Márcia de Borba Campos,
Alexandre M. Amory,
Isabel Harb Manssour
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/wvc.2020.13492
Subject(s) - braille , visually impaired , computer science , task (project management) , artificial intelligence , alphabet , robotics , character (mathematics) , robot , deep learning , artificial neural network , human–computer interaction , speech recognition , engineering , linguistics , philosophy , geometry , mathematics , systems engineering , operating system
Teaching computer programming to the visually impaired is a difficult task that has sparked a great deal of interest, in part due to its specific demands. Robotics has been one of the strategies adopted to help in this task. One system that uses robotics to teach programming for the visually impaired, called Donnie, has as its key part the need to detect Braille characters in a scaled-down environment. In this paper, we investigate the current state-of-the-art in Braille letter detection based on deep neural networks. For such, we provide a novel public dataset with 2818 labeled images of Braille characters, classified in the letters of the alphabet, and we present a comparison among some recent detection methods. As a result, the proposed Braille letters detection method could be used to assist in teaching programming for blind students using a scaled-down physical environment. The proposal of EVA (Ethylene Vinyl Acetate) pieces with pins to represent Braille letters in this environment is also a contribution.