Adaptive control using neural networks
Neural networks for control
IEEE Computer Graphics and Applications
A Learning-Based Prediction-and-Verification Segmentation Scheme for Hand Sign Image Sequence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time American Sign Language recognition from video using hidden Markov models
ISCV '95 Proceedings of the International Symposium on Computer Vision
ASL Recognition Based on a Coupling Between HMMs and 3D Motion Analysis
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Gesture-based interaction and communication: automated classification of hand gesture contours
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Signing Exact English (SEE): Modeling and recognition
Pattern Recognition
Signing Exact English (SEE): Modeling and recognition
Pattern Recognition
Analysis of hand movement variation related to speed in Japanese sign language
Proceedings of the 3rd International Universal Communication Symposium
Simple glove-based Korean finger spelling recognition system
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
Human-Computer interaction system with artificial neural network using motion tracker and data glove
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Fingerspelling recognition through classification of letter-to-letter transitions
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Vision-Based recognition of fingerspelled acronyms using hierarchical temporal memory
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
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An American Sign Language (ASL) finger spelling and an alphabet gesture recognition system was designed with ANN and constructed in order to translate the ASL alphabet into the corresponding printed and sounded English letters. The system uses a sensory Cyberglove and a Flock of Birds 3-D motion tracker to extract the gestures. The finger joint angle data obtained from strain gauges in the sensory glove define the hand shape while the data from the tracker describes the trajectory and orientation. The data flow from these devices is controlled by a motion trigger. Then, data is processed by an alphabet recognition network to generate the words and names. Our goal is to establish an ASL finger spelling system using these devices in real time. We trained and tested our system for ASL alphabet, names and word spelling. Our test results show that the accuracy of recognition is 96%.