Abstract:
In order to help the vast majority of the population learn sign language, specialists today frequently apply machine learning techniques. This project seeks to create a model that can recognize the letters in Bangladeshi Sign Language (BdSL) using a deep learning approach. This study is a comparison between the recognition of single-handed and dual-handed Bangladeshi Sign Language. Dataset, KU-BdSL is selected to train the single-handed BdSL and we selected dataset - BDSL 49 for dual-handed BdSL. We suggested pre-trained models of CNN based approach for the purpose of detection and classification. 30 different alphabets from single-handed BdSL and 36 different alphabets from dual-handed BdSL could be classified and recognized by our CNN models. Three pre-trained CNN models were employed. VGG16 outperformed the others by a wide margin. It correctly identified single-handed gestures with 98% accuracy. Regarding the ability to recognize dual-handed Bangladeshi Sign Language, VGG16 once again outperformed competitors with an accuracy of 90%.
