A Deep Learning Method for Braille Recognition

This paper mainly proposes a deep learning method-Stacked Denoising Auto Encoder (SDAE) to solve the problems of automatic feature extraction and dimension reduction in Braille recognition. In the construction of a network with deep architecture, a feature extractor was trained with unsupervised greedy layer-wise training algorithm to initialize the weights for extracting features from Braille images, and then a following classifier was set up for recognition. The experimental results show that by comparing to traditional methods, the constructed network based on the deep learning method can easily recognize Braille images with satisfied performance. The deep learning model can effectively solve the Braille recognition problem in automatic feature extraction and dimension reduction with a reduced preprocessing.