Kurdish Sign Language Recognition Using Convolutional Neural Network (CNN)

Abstract

The present study examines the obstacles encountered by the deaf population, with a specific emphasis on the growing importance of sign language in facilitating effective communication. The main mode of communication for deaf individuals is Sign Language (SL), which conveys meaning visually and expressively through facial expressions, hand movements, and body gestures. The objective of this project is to automate the recognition of sign language to improve accessibility and reduce reliance on interpreters. Specifically, this work focused on developing an alphabet recognition system for Kurdish Sign Language (KSL). Due to its many intricacies and resemblances to the Arabic script, KSL requires a robust recognition model. The proposed method utilizes Convolutional Neural Networks (CNN) trained on a real-world dataset to accurately recognize both numerical values and alphabetic characters in the Kurdish Sign Language (KSL). The real-time operation of the system enables rapid recognition of hand gestures, providing immediate textual output. The dataset used for training comprises 132,000 hand images, including 33 alphabetic signs and numeral signs from 0 to 9. The use of MediaPipe, a method for processing 3D images, significantly improves the efficiency of gesture detection. Multiple methodologies were investigated, and the integration of Convolutional Neural Networks (CNN), TensorFlow, and MediaPipe resulted in a remarkable accuracy of 99.87% with negligible dropout rates. This study establishes a foundation for enhanced communication and independence for the deaf community, representing a significance advancement in the automation of sign language recognition.