Introduction:
Sign language is the only way to communicate for speech-impaired people. But this sign
language is not known to normal people so this is the cause of barrier in communicating. This is the problem faced by
speech impaired people. In this paper, we have presented our solution which captured hand gestures with Kinect camera
and classified the hand gesture into its correct symbol.
Method:
We used Kinect camera not the ordinary web camera because the ordinary camera does not capture its 3d
orientation or depth of an image from camera however Kinect camera can capture 3d image and this will make
classification more accurate.
Result:
Kinect camera will produce a different image for hand gestures for ‘2’ and ‘V’ and similarly for ‘1’ and ‘I’
however, normal web camera will not be able to distinguish between these two. We used hand gesture for Indian sign
language and our dataset had 46339, RGB images and 46339 depth images. 80% of the total images were used for training
and the remaining 20% for testing. In total 36 hand gestures were considered to capture alphabets and alphabets from A-Z
and 10 for numeric, 26 for digits from 0-9 were considered to capture alphabets and Keywords.
Conclusion:
Along with real-time implementation, we have also shown the comparison of the performance of the various
machine learning models in which we have found out the accuracy of CNN on depth- images has given the most accurate
performance than other models. All these resulted were obtained on PYNQ Z2 board.
A comprehensive evaluation of deep models and optimizers for Indian sign language recognition
