Enhancing Model Accuracy for Keypoint-Based Sign Language Recognition using Optimized Neural Network Architectures

Sign language is a vital means of communication for millions, yet technological barriers still limit accessibility. To address this, we analyzed the existing deep learning model and identified key areas for enhancement. We expanded the neural network to improve learning capacity, replaced ReLU with LeakyReLU to avoid inactive neurons, and added batch normalization to maintain gradient stability throughout training. To reduce overfitting while preserving performance, we fine-tuned the dropout layer. We also improved preprocessing to filter out background noise, enhancing the system’s ability to accurately track gestures. The training process was accelerated using early stopping and model checkpointing in order to save the best-performing version possible without incurring unnecessary computation. The final leg was converting the model to run in TensorFlow Lite, so that it would be able to run efficiently on mobile and edge devices and hence making its real-world deployment possible. The results were demonstrative; greatly improved accuracy, enhanced stability, and decent real-time performance. With confusion matrices and ROC curves backing it, the improvement is measurable. But more importantly, this project is about inclusivity—what it means to bring people into technology more finely on behalf of the community.