Name
Abstract | ||
|---|---|---|
Sign language recognition is a challenging problem both when tracking continuous signs (communication mode) or single words (translation mode)1. We have developed a system that can recognize Greek sign language vocabulary in translation mode using Kinect technology. The sensor captures 3D hands movement trajectory and then a set of features in the form of body joints are fed to a classifier to recognize the input sign. Normalization is used to align test and stored trajectories using the dynamic time warping algorithm before matching is done using the Nearest-Neighbor approach. The low computational complexity of the involved algorithms allows for building a system with real-time response times. The system was evaluated with a sample of 5 individuals and is capable of recognizing 15 signs of the Greek sign language. Different configurations were tested and the best accuracy achieved was 99.33%. |
Year | Venue | Field |
|---|---|---|
2017 | PCI | Data mining,Greek Sign Language,Normalization (statistics),Dynamic time warping,Computer science,Speech recognition,Sign language,Classifier (linguistics),Vocabulary,Trajectory,Computational complexity theory |
DocType | Citations | PageRank |
Conference | 1 | 0.36 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nikolaos Gkigkelos | 1 | 1 | 0.36 |
| Christos Goumopoulos | 2 | 104 | 18.60 |