Improving Automatic Sign Language Translation with Image Binarisation and Deep Learning

Sign Language Translation (SLT) has been widely investigated to provide a futuristic solution to tackle human speech and hearing disability. Recent deep learning-based SLT models have redefined computer vision-based detection and classification to automatically translate the hand-gestured based sign language (SL) into natural language (NL) with higher accuracy. Unlike the existing models that directly learn from the natural image-sets, in this paper, we propose a 2D Convolutional Neural Network (CNN) model with customised hyper-parameters to be trained with binary SL image-sets. We thus introduce a binarisation step to preprocess the images of size $28 \times 28$ to feed the model. Preliminary results of our model trained with binarised image-set demonstrate its potential with an impressive classification accuracy of 99.99% on the NVIDIA Tesla K80 GPU environment (Google Colab) for an automatic SLT system.