World Braille Day

  The system presented in this project aims to Break down communication barriers for visually impaired individuals by translating written Braille into both text and audio formats . It leverages advanced machine learning techniques to accurately interpret Braille symbols and convert them into readable text. The system employs convolutional neural networks (CNNs) to improve the precision of Braille recognition . In addition, text-to-speech functionality is integrated to provide audio output, making the system more accessible. This project highlights the power of machine learning in creating inclusive solutions that cater to diverse user needs. Braille Recognition Using Convolutional Neural Network .

In this study , we have developed a hardware-optimized rotating disk-based Braille display . It requires a fewer actuators compared to the commercially available Braille notetakers that rely on piezoelectric actuators or electromagnets. An evaluation system was also constructed to quantify the accuracy and response time of the Braille output process using this newly developed display . The Braille display utilizes a Braille-based disk structure that indirectly represents braille patterns. Experimental results demonstrated that the rotational accuracy of the disk was within 2.7°, and a bidirectional rotation reduced the rotation pulse up to 200 compared to unidirectional rotation. Furthermore, the mean output time for a set of ten frequently used English words was measured to be 1600 ms. When YOLO V8-n AI (artificial intelligence) library was used to detect the characters and the position of a user’s finger model, the system consistently exhibited an average confidence level of ∼86 %...