Unveiling Expressions Automating Bharatnatyam Mudra Recognition through Advanced Computer Vision Techniques

We propound an innovative approach to mudra recognition, achieving promising results in interpreting mudra gestures from video input.Our methodology includes pre-processing techniques like resizing, normalization, and data augmentation, enhancing input data quality.Convolutional Neural Networks (CNNs) effectively detect and extract hand regions from images, while integrating pre-trained models like VGG or Res Net further improves performance.Additionally, we utilize Recurrent Neural Networks (RNNs), specifically LSTM or GRU units, to capture temporal dynamics in mudra gestures, addressing challenges like vanishing gradients and modelling longrange dependencies.Multimodal fusion techniques combine spatial and temporal features through concatenation and element-wise addition, with attention mechanisms prioritizing salient information, enhancing recognition accuracy for complex mudra gestures.Continuous evaluation using metrics like accuracy, precision, recall, and F1-score, alongside cross validation techniques, confirms the model's reliability and robustness across varied dataset subsets.The trained model is successfully implemented for real-time sign language recognition, featuring a user-friendly interface to ensure accessibility and ease of use while considering latency requirements.our comprehensive approach to mudra recognition integrates spatial and temporal Features effectively demonstrating reliability in real-world applications such as sign language recognition.