This article presents an interactive hand shape recognition user interface for American Sign Language (ASL) finger-spelling. The system makes use of a Microsoft Kinect device to collect appearance and depth images, and of the OpenNI+NITE framework for hand detection and tracking. Hand-shapes corresponding to letters of the alphabet are characterized using appearance and depth images and classified using random forests. We compare classification using appearance and depth images, and show a combination of both lead to best results, and validate on a dataset of four different users. This hand shape detection works in real-time and is integrated in an interactive user interface allowing the signer to select between ambiguous detections and integrated with an English dictionary for efficient writing.
Nicolas Pugeault et al. REF suggest a system for recognition of the hand configuration in the context of ASL, using the Microsoft Kinect to collect information about appearance and depth, and the OpenNI + NITE framework to detect and track the hand.
Spelling it out: Real-time ASL fingerspelling recognition
2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
Computer Science