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

fYear

2005

fDate

29 Aug.-1 Sept. 2005

Firstpage

740

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition, 2005. Proceedings. Eighth International Conference on

ISSN

1520-5263

Print_ISBN

0-7695-2420-6

Type

conf

DOI

10.1109/ICDAR.2005.195

Filename

1575643