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This paper presents a methodology for recognizing Indian Sign Language (ISL) gestures using deep learning techniques, specifically through the use of depth and RGB data captured by a Microsoft Kinect and normal RGB cameras. The authors achieved high classification accuracies of 98.81% for static gestures and 99.08% for dynamic gestures, while also addressing the limitations of using bulky depth cameras by implementing semantic segmentation with U-Net and ResNet architectures. The study aims to improve commu

Author

hrishikesh sanap

Language

EN