Title :
Recognition for large sets of handwritten mathematical symbols
Author :
Watt, Stephen M. ; Xie, Xiaofang
Author_Institution :
Dept. of Comput. Sci., Univ. of Western Ontario, London, Ont., Canada
fDate :
29 Aug.-1 Sept. 2005
Abstract :
Natural and convenient mathematical handwriting recognition requires recognizers for large sets of handwritten symbols. This paper presents a recognition system for such handwritten mathematical symbols. We use a pre-classification strategy, in combination with elastic matching, to improve recognition speed. Elastic matching is a model-based method that involves computation proportional to the set of candidate models. To solve this problem, we prune prototypes by examining character features. To this end, we have defined and analyzed different features. By applying these features into an elastic recognition system, the recognition speed is improved while maintaining high recognition accuracy.
Keywords :
document image processing; feature extraction; handwritten character recognition; character features; elastic matching; elastic recognition system; handwritten mathematical symbol recognition; mathematical handwriting recognition; symbol classification; Computer science; Feature extraction; Handwriting recognition; Mathematics; Performance analysis; Personal communication networks; Personal digital assistants; Prototypes; Smoothing methods; Writing;
Conference_Titel :
Document Analysis and Recognition, 2005. Proceedings. Eighth International Conference on
Print_ISBN :
0-7695-2420-6
DOI :
10.1109/ICDAR.2005.195
