American Sign Language Recognition Using Machine Learning and Computer Vision

<p>Speech impairment is a disability which affects an individual’s ability to communicate using speech and hearing. People who are affected by this use other media of communication such as sign language. Although sign language is ubiquitous in recent times, there remains a challenge for non-sign language speakers to communicate with sign language speakers or signers. With recent advances in deep learning and computer vision there has been promising progress in the fields of motion and gesture recognition using deep learning and computer vision-based techniques. The focus of this work is to create a vision-based application which offers sign language translation to text thus aiding communication between signers and non-signers. The proposed model takes video sequences and extracts temporal and spatial features from them. We then use Inception, a CNN (Convolutional Neural Network) for recognizing spatial features. We then use an RNN (Recurrent Neural Network) to train on temporal features. The dataset used is the American Sign Language Dataset.</p>

Name thesis:degree_name

Master of Science in Computer Science (MSCS)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year dc:date.available

2019

Authors dc:creator

• Bantupalli, Kshitij
• Xie, Ying

Contributors dc:contributor

• Dr Selena He

• machine learning
• computer vision
• neural network
• Robotics

Repository record dc:identifier

https://digitalcommons.kennesaw.edu/cs_etd/21

OAI identifier oai:identifier

oai:digitalcommons.kennesaw.edu:cs_etd-1024