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Varunshiyam
Is your feature request related to a problem? Please describe.
Effective communication with the deaf community is crucial for inclusivity, but traditional methods often present barriers. This project aims to bridge this communication gap by developing a robust American Sign Language (ASL) recognition system using deep learning. The challenge lies in accurately classifying diverse ASL expressions from images to enable real-time translation and facilitate seamless communication.
Describe the solution you'd like
This project demonstrates the application of transfer learning to classify American Sign Language (ASL) expressions. Two popular CNN architectures, InceptionV3 and ResNet50, are employed as base models with pre-trained weights from ImageNet. The models are fine-tuned on an ASL dataset, leveraging their learned feature representations for improved accuracy. The training process includes data augmentation and employs the Adam optimizer with a categorical cross-entropy loss function. The performance of both models is evaluated and compared, showcasing the effectiveness of transfer learning for ASL classification and its potential for communication support applications.
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