An opportunity to bridge the communication gap between the deaf community and common people
Sign language is the most popular and effective way of communication among hard hearing and normal
people. Normal people find little difficulty in understanding and interpreting the meaning of sign language
expressed by the hearing impaired, it is inevitable to have an interpreter for the translation of sign language.
Understanding sign language is the primary enabler in helping hard of hearing people with the rest of
society. since most people do not know sign language and interpreters are very difficult to come by we have
come up with a real-time method using neural networks for fingerspelling based on American sign language.
Our App is an effort toward studying the challenges in the classification of characters in Sign Language. So,
The app aims at building a machine learning model that will be able to classify the various hand gestures used for
fingerspelling in sign language. In this user-independent model, classification machine learning algorithms
are trained using a set of image data. For this, we are using the Convolutional Neural Network algorithm to
train the model. An attempt is made to increase the accuracy of the model.