Tailor: creating custom user interfaces based on gesture
UIST '90 Proceedings of the 3rd annual ACM SIGGRAPH symposium on User interface software and technology
An Engine for Cursive Handwriting Interpretation
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Kullback Leibler divergence based curve matching method
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
A lightweight multistroke recognizer for user interface prototypes
Proceedings of Graphics Interface 2010
Multi-stroke freehand text entry method using OpenVG and its application on mobile devices
Edutainment'06 Proceedings of the First international conference on Technologies for E-Learning and Digital Entertainment
The 1¢ Recognizer: a fast, accurate, and easy-to-implement handwritten gesture recognition technique
Proceedings of the International Symposium on Sketch-Based Interfaces and Modeling
The impact of motion dimensionality and bit cardinality on the design of 3D gesture recognizers
International Journal of Human-Computer Studies
Interpretation of strokes in radial menus: The case of the KeyScretch text entry method
Journal of Visual Languages and Computing
| Hi-index | 0.00 |
Dynamic programming has been found useful for performing nonlinear time warping for matching patterns in automatic speech recognition. Here, this technique is applied to the problem of recognizing cursive script. The parameters used in the matching are derived from time sequences of x-y coordinate data of words handwritten on an electronic tablet. Chosen for their properties of invariance with respect to size and translation of the writing, these parameters are found particularly suitable for the elastic matching technique. A salient feature of the recognition system is the establishment, in a training procedure, of prototypes by each writer using the system. In this manner, the system is tailored to the user. Processing is performed on a word-by-word basis after the writing is separated into words. Using prototypes for each letter, the matching procedure allows any letter to follow any letter and finds the letter sequence which best fits the unknown word. A major advantage of this procedure is that it combines letter segmentation and recognition in one operation by, in essence, evaluating recognition at all possible segmentations, thus avoiding the usual segmentation-then-recognition philosophy. Results on cursive writing are presented where the alphabet is restricted to the lower-case letters. Letter recognition accuracy is over 95 percent for each of three writers.