For the minority of people who are speech and hearing impaired, communication is essential for self-expression. Despite the availability of numerous assistance, such as visual aids or interpreters, these approaches can be costly or time-consuming. Deaf and dumb people mostly utilize sign language to communicate with one other and within their own community. Since they are mute, they communicate with hand gestures in this language. “Sign language recognition” (SLR) is a process of identifying & learning hand motions, with the goal of producing text for each associated gesture. These hand movements have been trained and detected using a multi-headed “convolutional neural network” (CNN) model using the well-known sign language MNIST dataset from Kaggle. The algorithm achieved an 80–90% success rate in identifying the signs based on the provided motions.