Sign Language Recognition using Depth Data and CNN

International Journal of Computer Science and Engineering
© 2019 by SSRG - IJCSE Journal
Volume 6 Issue 1
Year of Publication : 2019
Authors : Lakshman Karthik Ramkumar, Sudharsana Premchand, Gokul Karthi Vijayakumar

How to Cite?

Lakshman Karthik Ramkumar, Sudharsana Premchand, Gokul Karthi Vijayakumar, "Sign Language Recognition using Depth Data and CNN," SSRG International Journal of Computer Science and Engineering , vol. 6,  no. 1, pp. 9-14, 2019. Crossref,


Sign Language Recognition is a project which is primarily focused on recognizing gestures (used by Deaf and Dumb) and process them into sentences, thereby making communication of such people easier with the common people. The project uses "Intel Real Sense" technology (Intel Real Sense Robotic Development Kit), which captures depth images of gestures made the user. Depth images are useful for making highly precise predictions on what is being communicated by the user. Early works used different kind of image convolution to form feature vectors based on a single RGB image of a hand. The authors use wavelet families, computed on edge images, as features to train a Neural Network for 24 sign classification. Haarlet-like features, computed on grey-scale images and on silhouettes were used for classification of 10 hand shapes. Principle Component Analysis (PCA) was applied directly on images to derive a subspace of hand poses, which is then used to classify the hand poses. A modification of HOG descriptors is employed to recognize static signs of the British Sign Language. SIFT-feature based description was used to recognize signs of ASL. All these methods depend heavily on the lighting conditions, subject’s appearance and Background. This project uses Convolutional Neural Networks for classifying gestures using depth data and its features for more accuracy


ASL, Recognition, Depth Data, RealSense, Gesture Recognition.


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