Action Recognition for American Sign Language
In this research, we present our findings to recognize American Sign Language from series of hand gestures. While most researches in literature focus only on static handshapes, our work target dynamic hand gestures. Since dynamic signs dataset are very few, we collect an initial dataset of 150 videos for 10 signs and an extension of 225 videos for 15 signs. We apply transfer learning models in combination with deep neural networks and background subtraction for videos in different temporal settings. Our primarily results show that we can get an accuracy of $0.86$ and $0.71$ using DenseNet201, LSTM with video sequence of 12 frames accordingly.
Code (0)
등록된 구현이 없습니다.
Tasks
Action RecognitionTransfer LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Improving American Sign Language Recognition with Synthetic Data
Real-Time and Robust Method for Hand Gesture Recognition System Based on Cross-Correlation Coefficient
Hand gesture recognition possesses extensive applications in virtual reality, sign language recognition, and computer games. The direct interface of hand gestures provides us a new way for communicating with the virtual …
Gesture RecognitionHand Gesture RecognitionHand-Gesture RecognitionImage Segmentation+2Isolated Sign Recognition using ASL Datasets with Consistent Text-based Gloss Labeling and Curriculum Learning
We present a new approach for isolated sign recognition, which combines a spatial-temporal Graph Convolution Network (GCN) architecture for modeling human skeleton keypoints with late fusion of both the forward and backw…
Challenges for Linguistically-Driven Computer-Based Sign Recognition from Continuous Signing for American Sign Language
There have been recent advances in computer-based recognition of isolated, citation-form signs from video. There are many challenges for such a task, not least the naturally occurring inter- and intra- signer synchronic …
The American Sign Language Knowledge Graph: Infusing ASL Models with Linguistic Knowledge
Language models for American Sign Language (ASL) could make language technologies substantially more accessible to those who sign. To train models on tasks such as isolated sign recognition (ISR) and ASL-to-English trans…