Bangla Sign Language Recognition using YOLOv5
Mainul Karim, Maun Ali, Md. Ferdous Hasan, Most. Fatema-Tuj-Jahra, Syeda Sumaia Sultana, Dewan Md. Farid · 2023
Bangla Sign Language (BdSL), also known as Bengali or Bangladeshi Sign Language, is the sign language that is commonly used for communication with the deaf people in Bangladesh and some parts of India, e.g., West Bengal (formally known as Calcutta), Assam, Tripura, and the Andaman and Nicobar Islands. Bangla is one of the sweetest languages in the world, according to the UNESCO survey, and the 7th most spoken language in the world. In Bangladesh, about 13.7 million people have a hearing problem, and the Bangladesh National Federation of the Deaf (BNFD) was established on December 25, 1963, to provide training, skill development, and education to deaf people. In this study, we have applied transfer learning in machine learning for BdSL recognition that reuses a pre-trained classifier named YOLOv5 to detect and classify BdSL. Transfer learning speeds up the training process with a minimum number of instances and improves classification performance. Transfer learning for deep learning has become very popular for solving computer vision and natural language processing (NLP) tasks nowadays. Initially, we collected a 24.2 GB video of BdSL from SignBD-word and extracted images from the video using the FFmpeg multimedia framework. Then, we annotated the images using the LalelImg image annotation tool. Next, we checked the image annotation and built the model using YOLOv5. YOLOv5 is a family of compound-scaled object detection models, which stands for "You Only Look Once", commonly used for detecting objects by separating images into a grid structure. Finally, we have implemented the prototype with an average accuracy of 91.62%.