VGG Induced Deep Hand Sign Language Detection

TL;DR

This paper presents a deep learning system using VGG-16 for hand gesture recognition to aid sign language communication, achieving high accuracy through transfer learning and data augmentation.

Contribution

It introduces a novel hand gesture recognition system employing VGG-16 with transfer learning and data augmentation for improved accuracy.

Findings

Achieved around 98% accuracy on validation data.

Validated the model using the NUS dataset and a custom API-based dataset.

Demonstrated effectiveness of transfer learning and data augmentation in gesture recognition.

Abstract

Hand gesture recognition is an important aspect of human-computer interaction. It forms the basis of sign language for the visually impaired people. This work proposes a novel hand gesture recognizing system for the differently-abled persons. The model uses a convolutional neural network, known as VGG-16 net, for building a trained model on a widely used image dataset by employing Python and Keras libraries. Furthermore, the result is validated by the NUS dataset, consisting of 10 classes of hand gestures, fed to the model as the validation set. Afterwards, a testing dataset of 10 classes is built by employing Google's open source Application Programming Interface (API) that captures different gestures of human hand and the efficacy is then measured by carrying out experiments. The experimental results show that by combining a transfer learning mechanism together with the image data augmentation, the VGG-16 net produced around 98% accuracy.