<![CDATA[Sign language recognition plays a crucial role infacilitating communication for individuals with hearingimpairments. This paper presents a deep learning-basedapproach for recognizing Bahasa Isyarat Indonesia (BISINDO),the sign language used in Indonesia. The proposed systememploys convolutional neural networks (CNNs) and recurrentneural networks (RNNs) to automatically extract features fromsign language gestures and classify them into correspondinglinguistic units. The dataset used for training and evaluationconsists of annotated BISINDO sign language videos.Preprocessing techniques such as normalization andaugmentation are applied to enhance the robustness of themodel. Experimental results demonstrate the effectiveness of theproposed approach in accurately recognizing BISINDO signlanguage gestures, achieving state-of-the-art performancecompared to existing methods. The developed system showspromising potential for real-world applications in enhancingcommunication accessibility for the hearing-impairedcommunity in Indonesia.]]>
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