American Sign Language Recognition System for Numerical and Alphabets

Sign language is a helpful visual technique that can be utilized by individuals who have difficulty communicating verbally. This language is used frequently by people as well. However, the vast majority of individuals don't understand sign language very well, and as a result, they frequently struggle when attempting to communicate with someone who is fluent in it. The community of people who have trouble speaking or hearing will benefit from an initiative called the Sign Language Recognition System. It is extremely challenging for deaf mutes to communicate, which is why we require a linguistic framework to understand what it is that they are attempting to express. In light of this, we present in this research an American Sign Language (ASL) handwriting translator that is built on convolutional neural networks. Instructing computers to recognize specific characters is one way to accomplish this goal. The Support Vector Machine (SVM) and the Convolutional Neural Network are two types of artificial neural networks that are being utilized in our project we have suggest the identification of American Sign Language. In addition, the Decision Tree and Voting Classifier are applied to the dataset in order to do an analysis. We have demonstrated the design and execution of an American Sign Language (ASL) translator that also incorporates fingerspelling as a direct consequence of this.