Deep Learning for Indonesian Sign Language Recognition: Methods, Trends, and Public Dataset

Indonesian Sign Language consists of two systems, Sistem Isyarat Bahasa Indonesia (SIBI) and Bahasa Isyarat Indonesia (BISINDO), which serve as the primary means of communication for individuals with hearing disabilities in Indonesia. Recent advances in computer vision and deep learning have driven significant research on automatic sign language recognition using visual data. This review analyzes studies published between 2020 and 2025 to identify research trends, dataset characteristics, deep learning architectures, and reported performance in SIBI and BISINDO recognition. Based on 18 selected studies, the review reveals a clear disparity between static and dynamic recognition tasks. Static alphabet recognition using CNN-based and object detection models consistently achieves high accuracy in controlled environments. In contrast, dynamic recognition for words and continuous signing remains considerably more challenging due to temporal complexity, signer variability, and environmental factors. The findings also highlight limitations in publicly available datasets, particularly for sentence-level dynamic recognition. Overall, this review synthesizes current challenges and research opportunities, guiding the future development of robust, deployable Indonesian Sign Language recognition systems.

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