Sign Language Detection Application to Facilitate Communication for Speech and Hearing Impaired Individuals Based on Computer Vision Technology Using Inception Resnetv2 - Arfriandi, Arief; Prabowo, Yoga Agung; Najuda, Moh Dafi; Puspita, Tri Anggi Ratna; Virnanda, Shakira Wahyu; Sunardiyo, Said (Rekayasa, 2022)
Communication is a fundamental human need, yet not everyone possesses perfect communication abilities. People Communication is a fundamental human need, yet individuals with speech and hearing impairments face challenges due to the limited understanding of sign language among the general public. This study applies Artificial Intelligence and Computer Vision to enhance communication accessibility by detecting hand gestures and converting them into text. The lack of real-time sign language translation remains a barrier for individuals with disabilities. Existing systems often struggle with accuracy and device compatibility. This research develops and evaluates HARDISC, an Android-based application that recognizes letters A–Z through hand movement detection using a camera. The goal is to provide an effective and inclusive communication tool for the speech and hearing impaired. HARDISC utilizes Transfer Learning with Inception ResNetV2 and VGG16 for gesture classification. Image processing enables the camera to detect and translate hand movements into text. Model evaluation was based on accuracy, loss values, and device compatibility. Results show Inception ResNetV2 achieved 98.98% accuracy with a 0.0417 loss value, while VGG16 recorded 99.40% accuracy with a 0.0146 loss value, demonstrating high performance. HARDISC is compatible with Android KitKat 4 to Android 12, ensuring accessibility. This application provides an innovative, real-time solution to bridge communication gaps for individuals with speech and hearing impairments, improving their interaction with the general public.