Filipino Sign Language Recognition using Deep Learning

Abstract

The Filipino deaf community continues to lag behind the fast-paced and technology-driven society in the Philippines. The use of Filipino Sign Language (FSL) has contributed to the improvement of communication of deaf people, however, the majority of the population in the Philippines do not understand FSL. This project utilized computer vision in obtaining the images and Convolutional Neural Network (CNN) ResNet architecture in building the automated FSL recognition model, with the goal of bridging the communication gap between the deaf community and the hearing majorities. In the experimentation, the dataset used are static images generated from a signer which gestured Filipino number signs which range from (0-9). Based on experimentation, the best-achieved performance is on fine-tuned ResNet-50 model which obtained a validation accuracy as high as 86.7% when the epoch value equals 15. For future work, real-time FSL recognition will be implemented and more data will be collected to enable recognition of Filipino alphabets, basic phrases, and common greetings.