Created on 17th March 2024
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Our pioneering sign language recognition model surpasses traditional approaches where static image datasets are utilised but we have made our ML model that utilizes the nuanced gestures and precise movements of the joints as it's dataset.
Model Optimization Challenges:
Initially, our primary obstacle revolved around identifying an appropriate and finely-tuned model within a vast domain. From grappling with the challenge of overfitting in image classification to navigating the intricate complexities of YOLO, we found ourselves navigating a delicate balance of parameter trade-offs.
Data Scarcity in Action Classification:
Upon transitioning to action classification, we encountered a significant scarcity of datasets. This scarcity, akin to traversing a barren landscape, necessitated innovative solutions such as data augmentation and transfer learning to address.
Integration Challenges with Text-to-Speech and Language Translation:
Subsequently, as we endeavored to integrate text-to-speech and language translation modules, we encountered formidable challenges. The intricate process of handshaking between these modules revealed unforeseen complexities, exacerbated by the nuances and variations inherent in linguistic expression.
API Deployment Hurdles with Flask:
Finally, our journey culminated in the formidable task of deploying APIs using Flask. The suboptimal performance of Flask, particularly concerning tasks like video streaming and callback handling, presented significant hurdles that necessitated innovative solutions to overcome.
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