Deep Learning Strategy for Braille Character Recognition

• Published in: IEEE access Vol. 9; pp. 169357 - 169371
• Main Authors: Kausar, Tasleem, Manzoor, Sajjad, Kausar, Adeeba, Lu, Yun, Wasif, Muhammad, Ashraf, M. Adnan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Braille; Braille images; Character recognition; Computational modeling; Convolution; convolution neural networks; Convolutional neural networks; Datasets; Deep learning; image alignment; Image enhancement; Image recognition; inverted residual block; Machine learning; Model testing; Modules; Neural networks; Object recognition; People with disabilities; principal component analysis; User interfaces; Visual impairment; Wiener filtering
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3138240

People with vision impairment use Braille language for reading, writing, and communication. The basic structure of the Braille language consists of six dots arranged in three rows and two column cells, which are identified by visually impaired people using finger touch. However, it is difficult to memorize the pattern of dots that form the Braille characters. This research presents a novel approach for automatic Braille characters recognition. The designed approach works in two main stages. In first stage, image alignment & enhancement are performed using several image preprocessing techniques. In second stage, character recognition is performed with a proposed lightweight convolution neural network (CNN). As CNN shows promise for accurate recognition of optical characters. Therefore, we adopted several recently proposed state-of-the-art CNN networks for Braille characters' recognition. To make the networks light and improve their recognition performance, we proposed a strategy by replacing few modules in the original CNNs with an inverted residual block (IRB) module with less computational cost. The novelty of this work lies in CNN model design and output performance. We executed the effectiveness of the designed setup through experiments on two different publicly available benchmark Braille datasets obtained from visually impaired people. On the English Braille and Chinese double-sided Braille image (DSBI) datasets, the proposed model shows a prediction accuracy of 95.2% and 98.3%, respectively. The reported test time of model is about 0.01s for English and 0.03s DSBI Braille images. In comparison to state-of-the-art, designed method is robust, effective, and capable to identify the Braille characters efficiently. In future, the functional performance of the proposed Braille recognition scheme will be tested through accessible user interfaces.