A Smart System for Sign Language Recognition using Machine Learning Models

When people exchange information, thoughts, or sentiments via the medium of communication, it is called communication. Both parties must be conversant in the same language in order for communication to occur. On the other hand, those who are deaf or dumb must use various communication methods. If you can't hear, you have deafness; if you can't speak, you have dumbness. As a group, they use sign language to communicate, but most people don't see it as a valuable skill. To communicate with someone who is deaf or mute is difficult since not everyone is familiar with or understands sign language. You may develop a model using machine learning to get around this roadblock. It is possible to teach a model to recognize different gestures used in sign language and to translate those gestures into English. Many people will get the ability to converse with the deaf and dumb as a result of this. By using a camera to capture Sign Language data, we may then use machine learning techniques like Convolutional Neural Networks to the datasets. Some other well-known models have been compared to a new strategy that has been presented for the same problem. Principal Component Analysis (PCA), Histogram of Gradients (HOG), and Local Binary Patterns (LBP) are some of the pre-processing approaches employed. ORB, Canny edge detection, and the state of word approach are all used to create the new model. This preprocessed data is then sent through a variety of classifiers in order to provide useful findings (including Random Forests and Support Vector Machines, as well as Nave Bayes and Logistic Regression). The new models are substantially more accurate than the previous ones. The method achieves good accuracy even with a little dataset.