Korean Sign Language Alphabet Recognition Through the Integration of Handcrafted and Deep Learning-Based Two-Stream Feature Extraction Approach

Recognizing sign language plays a crucial role in improving communication accessibility for the Deaf and hard-of-hearing communities. In Korea, many individuals facing hearing and speech challenges depend on Korean Sign Language (KSL) as their primary means of communication. Many researchers have been working to develop a sign language recognition system for other sign languages. Still, little research has been done for KSL alphabet recognition due to the lack of dataset availability. Moreover, existing KSL recognition systems have faced significant performance limitations due to the ineffectiveness of the features. To overcome the challenges, we newly created a KSL alphabet dataset and introduced an innovative KSL recognition system employing a strategic fusion approach. In the proposed study, we combined joint skeleton-based handcrafted features and pixel-based resnet101 transfer learning features to overcome the limitations of feature effectiveness in traditional systems. Our system consists of two distinct feature extraction streams: the first stream extracts essential handcrafted features, emphasizing capturing hand orientation information within KSL gestures. In the second stream, concurrently, we employed a deep learning-based resnet101 module stream to capture hierarchical representations of the KSL alphabet sign. By combining essential features from the first stream with the hierarchical features from the second stream, we generate multiple levels of fused features with the goal of forming a comprehensive representation of KSL gestures. Finally, we fed the concatenated feature into the deep learning-based classification module for the classification. We conducted extensive experiments with the newly created KSL alphabet dataset, the existing KSL digit and the existing ArSL and ASL benchmark datasets. Our proposed model undeniably shows that our fusion approach substantially improves high-performance accuracy in both cases, which proves the system’s superiority.