Download Project Document/Synopsis
The desire for a computer-based solution is significant in this age of technology for deaf people. However, researchers have been working on the problem for quite some time, and the results are promising. Although interesting technologies for voice recognition are becoming available, there is currently no commercial solution for sign recognition on the market.
The gesture is a vital and meaningful mode of communication for the visually impaired person. So here is the computer-based method for regular people to understand what the differently-abled individual is trying to say. In our system, the user will perform the hand gestures or signs by turning on their camera, and the system will detect the sign and display it to the user.
In this sign language recognition system, a sign detector detects numbers, which can be easily extended to cover a wide range of other signs and hand signs, including the alphabet. In this system, we are using a machine learning model known as CNN. Convolutional Neural Networks (CNNs) can learn complicated objects and patterns because they have an input layer, an output layer, numerous hidden layers, and millions of parameters.
By turning on the camera, the user can perform the hand signs, and the system will detect the sign and display it to the user. Using hand signs, the individual may send out more information in a shorter amount of time.
Advantages
- Helps in learning hand signs.
- CNN is very satisfactory at picking up on design in the input image, such as lines, gradients, circles, or even eyes and faces.