Indian Sign Language converter using Convolutional Neural Networks
Nishi Intwala, Arkav Banerjee, Meenakshi Meenakshi, Nikhil Gala · 2019
People with hearing and speech impairments have to face a lot of difficulties while communicating with the general public. Being a minority, the sign language used by them is not known to a majority of people. In this paper, an Indian sign language converter was developed using a Convolutional Neural Network algorithm with the aim to classify the 26 letters of the Indian Sign Language into their equivalent alphabet letters by capturing a real time image of that sign and converting it to its text equivalent. First a database was created in various backgrounds and various image pre-processing techniques were used to make the database ready for feature extraction. After feature extraction, the images were fed into the CNN using the python software. Several real time images were tested to find the accuracy and efficiency. The results showed a 96% accuracy for the testing images and an accuracy of 87.69% for real time images.