Real-time Bangla Sign Language Detection using Xception Model with Augmented Dataset
Progya Paromita Urmee, Md. Abdullah Al Mashud, Jasmin Akter, Abu Shafin Mohammad Mahdee Jameel, Salekul Islam · 2019
Bangla Sign language (BdSL) is the communication language used by the deaf and dumb of Bangladesh. In this paper, we present an optimal approach to recognize BdSL in real-time. First, we have developed BdSLInfinite dataset, which consists of 2,000 images of 37 different signs. Using this dataset, a convolutional neural network (CNN) based model is trained using Xception architecture that achieves 98.93% accuracy over the test-set, with response time of 48.53 ms on average. To the best of our knowledge, our proposed method outperforms all existing BdSL recognition methods in terms of both accuracy and speed.