Translating Braille Patterns into Arabic Text Using a Convolutional Neural Network

Abstract

Considering optical Braille patterns has been investigated in several studies. There is a huge number of studies which analyzed Braille patterns in different natural languages. However, the Arabic patterns have not been examined as same as the other languages. This is due to the lack of the datasets of the Arabic patterns and the shortage of researches in this area. This study utilizes YOLOv11 model as a detection tool because of its relative effectiveness and the level of accuracy as well as the staged training approach with the AdamW optimizer and Automatic Mixed Precision. For the translation of the Arabic patterns into text, post-processing steps are performed including: detecting cells vertically clustered, horizontally sorted within a line, adaptively defined word boundaries, and corrected reading order of right-to-left.  In the analysis of experiments, the best findings achieved is 0.99 of all the precision, recall, and F1 scores. Moreover, the framework-level runtime indicates that the total processing time (inference + post-processing for text extraction) ranges between 29 and 82 ms per image. The proposed framework has been examined with a primary dataset of 5924 pages of images of Braille patterns of 45 classes of Arabic letters and diacritics. The yielded results show that the proposed framework is a robust approach toward effective, scalable, responsive Arabic Braille recognition (OBR) for assistive technologies to be mobile and wearable. By building the first dedicated corpus in Arabic Braille and providing an end-to-end recognition suite, this study laid the groundwork for future research and applications in the field. This research bridges the accessibility gap, so of allow sighted individuals to access content encoded in Braille.