Borno-Net: A Real-Time Bengali Sign-Character Detection and Sentence Generation System Using Quantized Yolov4-Tiny and LSTMs

Sign language is the most commonly used form of communication for persons with disabilities who have hearing or speech difficulties. However, persons without hearing impairment cannot understand these signs in many cases. As a consequence, persons with disabilities experience difficulties while expressing their emotions or needs. Thus, a sign character detection and text generation system is necessary to mitigate this issue. In this paper, we propose an end-to-end system that can detect Bengali sign characters from input images or video frames and generate meaningful sentences. The proposed system consists of two phases. In the first phase, a quantization technique for the YoloV4-Tiny detection model is proposed for detecting 49 different sign characters, including 36 Bengali alphabet characters, 10 numeric characters,…