Effective Recognition System of American Sign Language Alphabets using Machine Learning Classifiers, ANN and CNN

People who cannot talk are called audibly impaired, and they communicate with others through other means. The most popular method of communication is through sign language. American Sign Language (ASL) is the de-facto standard for sign languages taught globally. Automated sign language recognition tries to bridge the gap. Convolutional neural networks are the method of choice these days when it comes to the classification of multiclass images. To recognize ASL alphabets, we used a CNN, traditional machine learning classifiers, and an artificial neural network. The Sign Language MNIST dataset has a total of 34,627 image data, of which 27,455 and 7172 are training and test data respectively. Except for J and Z, the dataset comprises 24 alphabets. We used the training dataset to train our CNN, ANN, and other machine learning models. Then examined our proposed CNN model as well as other models including ANN on the test dataset to check how well they recognize ASL alphabets correctly. The traditional classifiers such as Linear Regression, Logistic Regression, Random Forest, SVM, and ANN were able to achieve an accuracy of 71.94%, 90.16%, 98.63%, 97.92%, 82.96% respectively whereas the proposed CNN model achieved 100 % of accuracy on the unseen test data.