Boosting ASL Communication with a Convolutional-Bidirectional LSTM Hybrid Model

Abstract

This American Sign Language Model represents an advanced deep learning model framework that employs convolutional, bilateral LSTM, and dense layers to analyze sequential input logically. By considering 58 million factors, the model exposes the substantial learning ability capable of detecting subtle patterns within sequential information. To extract hierarchical features, the convolutional layers are more proficient in supplementing with bidirectional LSTm for complete topical modeling and dense layers for additional modification. Implementing the Adam Optimizer at a learning rate of 1e-3, the model illustrates adaptive learning, which is more critical for optimizing parameter updates during training. Over 20 epochs, the virtual model achieved remarkable performance at a mean loss of 0.0065% and an accuracy of over 99%. Precise testing indicates maximum strong performance and adaption to a wide range of sequential data workloads. The suggested design emerges as a strong tool to improve the potential of deep learning for sequence information processing, making a massive contribution to the continuous evolution of advanced neural network architectures.