Modified CNN Model for Hand Gesture Recognition Using Sign Language

In this article an enhanced accuracy of hand gesture recognition using data augmentation is presented. The proposed model has base on the CNN with data augmentation to recognize static hand gestures. The model has tested on 7172 images after being trained on 27,455 images. The accuracy of the model using supplemented data was $99.76 \%$, which is nearly greater than the accuracy of the CNN model without augmentation ($86.87 \%$).