New Technique withConvolution Neural Networks (R- CNN's) Model for Hand Detection

Abstract

The ability to listen and say the word are the most important aspects of
communication, but many of us are unlucky because we were not born with this skill from God.
These people are deaf and dumb. Many studies are currently being conducted to address the
difficulties that these members of our society encounter in communicating with ordinary people.
It is extremely difficult for mute (deaf and dumb) people to communicate their information to the
general public. Because the average person is not adequately prepared to grasp various sign. It
gets very difficult to communicate between these two types of people. Our research is only for
the purpose of assisting these mute (deaf and dumb) people in leading a better life. Many
computer vision tasks involving human hands, such as hand pose estimation, hand identification
of gestures, human behavior analysis, and so on, are performed by humans. include hand
detection as a critical pre-processing technique. However, due to the diverse appearance
diversities of human hands, such Strong diffraction, weak resolution, fluctuating levels of light, a
variety of hand gestures, and complicated interactions between hands and things or other hands
are all factors to consider. such as (different hand forms, hand tracking, skin colors, scales,
illuminations, orientations, gesture analgesia), accurately detecting hands is a difficult activity. in
color pictures, as well as Interaction between humans and machines, recognizing of sign
languages and so on). To address this problem, a region-based convolution neural networks (RCNN's)
was used, in which hand regions are discovered and hand appearances are recreated
simultaneously using attributes derived from a region proposal. The R-CNN was shown to be
suitable for hand gesture detection with acceptable error.