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Computer Vision Based Sign Language Recognition Using 2D Convolution Neural Network

J. Sagaya Mary, M. Nachamai


Sign language recognition facilitates the interaction between the special children with hearing difficulty and normal people. Though this mission has the wide-ranging societal impression, it is quite challenging for the reason of extraneous mechanism to be carried on hand and adopting suitable traditional hand-crafted techniques for feature learning and classification. Computer vision based technique is useful in sign language recognition since it does not require any external gadgets. Instead of constructing complex handcrafted features, Convolution Neural Network (CNN) is capable to automate the process from feature learning to classification. Proposed work develops days of the week sign language recognition system using CNN and CPU acceleration. The model deployed 100% accuracy in training data, 93% in validation data and 92% in testing data. The aim of the paper is also to comprehend the hidden mechanism of automatic learning which reduces the human effort in machine learning a superset of deep learning.


Multilayer neural network, back-propagation, Convolution Neural Network, Deep Learning, Machine Learning.

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