Integrating Deep Learning and Python for Effective Sign Language Recognition

The advancement in technology has improved the accessibility of communications for a person with hearing impairment significantly. A full implementation approach for sign language recognition is provided based on deep learning techniques implemented in Python. Several neural network architectures have been explored to achieve a level of high accuracy in the recognition of ASL gestures from video data based on CNNs and LSTM networks. The dataset for this is pretty diverse sign language gestures, like there is a sign language gesture describing ‘cow’ and then another describing ‘panda’. The two models are then trained and tested based on the whole dataset. Apart from training and testing, we also discuss preprocessing techniques, data augmentation, optimization strategies to improve the performance of the model. We prove that the proposed system, in fact, is a real-time sign language recognition system, providing an intuitive interface for the users. Results indicate, therefore, a good integration extent of the deep learning frameworks with Python and bring considerable improvements concerning accuracy and efficiency of sign language interpretation-all leading to better communication and inclusion in cases among the deaf and hardof-hearing community.