A Computer Vision-Based System for Recognition and Classification of Urdu Sign Language Dataset for Differently Abled People Using Artificial Intelligence

Communication between normal people and deaf people is the most difficult part of daily life worldwide. It is difficult for a normal person to understand a word from the deaf one in their daily routine. So, to communicate with deaf people, different countries developed different sign languages to make communication easy. In Pakistan, for deaf people, the government developed Urdu Sign Language to communicate with deaf people. Physical trainers and experts are difficult to provide everywhere in society, so we need such a computer/mobile-based system to convert the deaf sign symbol into voice and written alphabet that the normal person can easily get the intentions of the deaf one. In this paper, we provided an image processing and deep learning-based model for Urdu Sign Language. The proposed model is implemented in Python 3 and uses different image processing and machine techniques to capture the video and transform the symbols into voice and Urdu writing. First, we get a video from the deaf person, and then the model crops the frames into pictures. Then, the individual picture is recognized for the sign symbol such as if the deaf showed a symbol for one, then the model recognizes it and shows the letter which he/she wants to tell. Image processing techniques such as OpenCV are used for image recognition and classification while TensorFlow and linear regression are used for training the model to behave intelligently in the future. The results show that the proposed model increased accuracy from 80% to 97% and 100% accordingly. The accuracy of the previously available work was 80% when we implemented the algorithms, while with the proposed algorithm, when we used linear regression, we achieved the highest accuracy. Similarly, when we used the TensorFlow deep learning algorithm, we achieved 97% accuracy which was less than that of the linear regression model. © 2023 Hira Zahid et al.