@inproceedings { , title = {A hybrid neural network/rule-based technique for on-line gesture and hand-written character recognition}, abstract = {A technique is presented which combines rule-based and neural network pattern recognition methods in an integrated system in order to perform learning and recognition of hand-written characters and gestures in realtime. The GesRec system is introduced which provides a framework for data acquisition, training, recognition, and gesture-to-speech transcription in a Windows environment. A recognition accuracy of 92.5\% was obtained for the hybrid system, compared to 89.6\% for the neural network only and 82.7\% for rules only. Training and recognition times are given for an able-bodied and a disabled user.}, conference = {Proceedings of the Fourth IEEE International Conference on Electronics, Circuits and Systems}, organization = {Cairo, Egypt}, publicationstatus = {Published}, url = {https://nottingham-repository.worktribe.com/output/1024332}, keyword = {gesture recognition, dissimilarity, similarity, segmentation, text-to-speech, gesture-to-speech, sign language, 3D tracking, Augmentative and Alternative Communication, AAC, human computer interaction, HCI}, author = {Craven, Michael P. and Curtis, K. Mervyn and Hayes-Gill, Barrie H. and Thursfield, C.D.} }