Transforming Sign Language into Text using Machine Language

The project "Transforming Sign Language to Text using Machine Learning" provides a technological communication bridge for those with hearing and speech impairments. There aren't many ways for people with speech and hearing impairments to communicate with others. Using sign language as a means of communication is one of these choices. Numerous technological advancements and much study have been made to help the blind and deaf. In this sign language identification paper, we have created a sign detector that can easily distinguish between the letters of the alphabet, numerals from 1 to 10, and a wide range of other signals and hand motions. The purpose of this research is to enable individuals to detect hand signals and gestures. To train a model for sign language translation, we will use a gesture recognition model. We have picked CNN as our learning model since it provides better accuracy than the other approaches, which will help people converse with others who are naturally hard of hearing and speech. If the epochs are taken between 1 and 10, we will have the optimal curve; otherwise, it may result in underfitting or overfitting. As the number of epochs grows, the number of times the weights in the neural network are modified. Optimal Curve and 100% accuracy are provided for 1 to 10 epochs.