SMART SIGN LANGUAGE RECOGNITION SYSTEM USING MEDIAPIPE
DOI:
https://doi.org/10.29121/shodhkosh.v4.i1.2023.5530Keywords:
Neural Network Model, Sign Language Recognition, Sign Language, Muteness, Deep Learning Models, Custom Sign Language.Abstract [English]
Sign language serves as a crucial mode of communication for individuals experiencing muteness, affording them a means to articulate their thoughts, emotions, and needs. The efficacy of sign language hinges on the precise interpretation of hand gestures, encompassing both standardized and personalized forms. This initiative seeks to address the communication hurdles faced by mute individuals. The primary aim is to augment communication channels by employing deep learning models for the precise prediction of both standardized and personalized hand gestures. To attain cutting-edge accuracy, two distinct models are employed. The initial model harnesses a meticulously trained Google model, pretrained on a dataset exceeding 30,000 images, seamlessly integrated into the project via the MediaPipe library. The second model is a bespoke deep learning model, crafted to align with project-specific requirements and facilitate iterative training through a runtime UI. This ensures the recognition of an extensive array of hand gestures with custom text output. The project maintains efficiency by incorporating TensorFlow Lite and other complementary libraries. By enhancing communication for individuals with muteness, this endeavor has the potential to significantly elevate their quality of life.
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Copyright (c) 2023 Dr. Sunil Rathod, Dr. Soumitra Das, Dr. Vikas Nandgaonkar
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