Challenges faced by students and professionals with visual impairment in reading and understanding digital content have always been a great area of concern, particularly in subjects like mathematics and science due to their high dependency on special symbols. On one hand, useful tools are limited by complex formats; on the other hand, most available tools are directional dependent as standard textual information does not support an effective tactile form of knowledge. This research therefore, developed Optical Braille Recognition Methodology (OBRM) for transforming printed documents into Braille language files. The proposed methodology integrates image segmentation with multistage AI processing to enhance recognition of textual content and interpretation of mathematical and special symbols. A novel key-element quantification strategy reduces overall complexity and minimizes memory usage. Multi-format document acquisition followed pre-processing steps to enhance quality and remove noises before starting OCR processing that again used additional Neural Network backend for structured parsing which finally ended the tactile symbol generation stage, where new 3-bit encoding implemented readable form creation in a BRF extension file output. Even the semantic structure organized text making similar word or symbol placed together assists easier understanding during the later tactile reading learning process. A user-experience survey involving target users was organized and carried out on the system as a beta test.
Sindhu et al. (Thu,) studied this question.