The Comparison of Distance Models for Real-Time Recognition Systems of BISINDO Sign Language Letters
The Comparison of Distance Models for Real-Time Recognition Systems of BISINDO Sign Language Letters

Summary/Abstract: Indonesian Sign Language (BISINDO) is an essential means of communication for deaf people in Indonesia. However, communication challenges persist for the general public, who lack sign language proficiency. To bridge this gap, a real-time BISINDO recognition system is essential. This research compares the performance of several distance models in recognizing BISINDO letters using a camera-based recognition system. The study evaluated three distance methods City Block, Canberra, and Chebyshev applied to shape feature extraction through chain code analysis. Testing was conducted under both indoor and outdoor conditions to assess system robustness. The results revealed that the Chebyshev distance method achieved the highest accuracy in indoor scenarios, particularly when participants wore white clothing, reaching an accuracy of 100%. Meanwhile, in outdoor conditions, the Canberra distance method outperformed the others, achieving the highest accuracy of 96.15% with white clothing. However, outdoor accuracy was generally lower due to uncontrollable lighting, which affected recognition. Despite these challenges, the system demonstrated its capability to recognize BISINDO letters effectively in both environments. This research highlights the importance of choosing the appropriate distance model and controlling external factors, such as lighting, to improve system performance. The findings are expected to significantly advance efficient and reliable sign language recognition systems in Indonesia, thereby enhancing communication between the deaf community and the broader population. This development is a step forward in promoting inclusivity and accessibility in everyday interactions.

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