American Sign Language

Speech impairment constitutes a challenge to an individual's ability to communicate effectively through speech and hearing. To overcome this, affected individuals’ resort to alternative modes of communication, such as sign language. Despite the increasing prevalence of sign language, there still exists a hindrance for non-sign language speakers to effectively communicate with individuals who primarily use sign language for communication purposes. Sign languages are a class of languages that employ a specific set of hand gestures, movements, and postures to convey messages. Deaf and Hard-of-Hearing (DHH) people require some type of technology to convey their feelings or if I rephrase it, we are the people required to learn sign language to be able to understand what a DHH person is trying to convey, and with a motive to convey our concern towards the same, we are here with our project.The main motive behind this project is to provide such users with a user-friendly interface to convey their messages to a person who doesn’t understand sign language using deep learning technology. The objective of this project is to conduct an analysis and recognition of various alphabets present in a database of sign images in real-time using the camera of the device to form sentences. This project will include the formation of a dataset on which such a model can be trained, that is we will be creating our dataset and won’t be using any existing one. Further, using Convolutional Neural nets will be used to train a classifier to predict the alphabet gestured by the user. As a further extension to the project, our interface will also include a feature to show exactly the opposite of the same. It will be able to generate a sign sequence on the basis of an input sentence in order to educate people about using sign language. This function will help the user to communicate with the DHH person using their own method i.e., Sign language.Our dataset contains a series of different folders containing different format of images, like the root images, augmented images, preprocessed images (grayscale, histogram equalization, binarized images), and skeleton mediapipe landmark images for various classes of the alphabets.