Sign Language Digit Recognition Using Different Convolutional Neural Network Model

Main Article Content

Md. Bipul Hossain
Apurba Adhikary
Sultana Jahan Soheli


An enormous number of world populations in current time are unique in that sense that they have no broad language because of the absence of their hearing capability. The people with hearing impairment have their own language called Sign Language however it is hard for understanding to general individuals [1]. Sign digits are additionally a significant piece of gesture based communication. So a machine interpreter is important to permit them to speak with general individuals. For making their language justifiable to general individual’s computer vision based arrangements are notable these days. In this exploration of work we target to develop a model based on CNN to deal with the recognition of Sign Language digits. A dataset of 10 classes is used to train (70%), validation (20%) and test (10%) of the network. We consider three different models of CNN network to train and test the accuracy of sign digit. Among the three model transfer learning based pre-trained CNN performs better with test accuracy of 92%.

Sign language, convolutional neural network, transfer learning, digit recognition.

Article Details

How to Cite
Hossain, M. B., Adhikary, A., & Soheli, S. J. (2020). Sign Language Digit Recognition Using Different Convolutional Neural Network Model. Asian Journal of Research in Computer Science, 6(2), 16-24.
Original Research Article


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[Accessed date:01-05-20]