Affiliation:
1. Sinhgad College of Engineering, Pune, Maharashtra, India
Abstract
People suffering from speech impairment can't communicate using hearing and speech methods, they believe signing for communication. Sign language is employed among everybody who is speech impaired, but they find a tough time in communicating with people which are non-signers (people aren’t proficient in sign language). So, requirement of a symbol language interpreter may be a must for speech impaired people. There has been favourable progress within the field of gesture recognition and motion recognition with current advancements in deep learning. There has been quite a significant development in computer vision which would enable us to easily track the hand gestures. The proposed system tries to try to a true time translation of hand gestures into equivalent English text. This system takes hand gestures as input through video and translates it text which might be understood by a non-signer. There will be use of CNN for classification of hand gestures. By deploying this technique, the communication gap between signers and non-signers will be reduced and they will be easily able to communicate with normal people.
Reference6 articles.
1. Karen Simonyan & Andrew Zisserman “Very Deep Convolutional Networks for Large-Scale Image Recognition
2. LeCun, Y., Bengio, Y., & Hinton, G.” Deep learning.” vol 521 Nature, 521 pp 436-444 May 2015
3. Daniel Svozil, Vladimir KvasniEka, JiE Pospichal “Introduction to multi-layer feed-forward neural networks”,
4. Alper Yilmaz, Omar Javed, Mubarak Shah, “Object Tracking: A Survey”, ACM Computing Survey, Vol 38 No.4,
5. Deshmukh K.S., Shinde G. N, “An Adaptive Color Image Segmentation”, Electronic letters on Computer Vision and Image