Sign Language Detection using Deep Learning


  • Himesh Mishra, Rahul Rathour, Mohd Abuzar Suhail, Daya Shankar Srivastava & Mr. Arjun Singh GREATER NOIDA INSTITUTE OF TECHNOLOGY, GREATER NOIDA



Sign Language, Deep learning, Communication, LSTM, GRU


According to the 2011 Census, In India, out of the total population of 121 crores, approximately 2.68 Crore humans are 'Disabled' (2.21 % of the whole population)). Sign Language serves as a means for these people with special needs to communicate with others, but it is not a simple task. This barrier to communication has been addressed by researchers for years. The goal of this study is to demonstrate the Mobile Nets model's experimental performance on the TensorFlow platform when training the Sign language. Language Recognition Model, which can drastically reduce the amount of time it takes to learn a new language. Classification of Sign Language motions in terms of time and space Developing a portable solution for a real-time application. The Mobile net V2 Model was trained for this purpose and an Accuracy of 70% was obtained. The four different sequential combinations of LSTM and GRU (as there are two layers of LSTM and two layers of GRU) were used with our own dataset, IISL2020. The proposed model, consisting of a single layer of LSTM followed by GRU, achieves around 97% accuracy over 11 different signs. This method may help persons who are unaware of sign language to communicate with persons whose speech or hearing is impaired.


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How to Cite

Sign Language Detection using Deep Learning. (2024). Knowledgeable Research: A Multidisciplinary Journal, 2(11), 1-5.

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