An Improved SVM-HMM Based Classifier for Online Recognition of Handwritten Chemical Symbols

Author

Shi, Guangshun ; Zhang, Yang

Author_Institution

Inst. of Machine Intell., Nankai Univ., Tianjin, China

fYear

2010

fDate

21-23 Oct. 2010

Firstpage

1

Lastpage

5

Abstract

In this paper, we propose an improved double-stage classfier for online recognition of handwritten chemical symbols. In the first stage, SVM based classifier is used to roughly classify chemical symbols into Non-Ring Structure(NRS) and Organic Ring Structure(ORS). Then, HMM based classifier is used for fine classification at the second stage. During the fine classification stage, we use the frequency domain feature instead of the commonly used Geometrical or Statistical feature to perform recognition task. In addition, to improve the accuracy of the ORS symbols and the speed of processing, we propose a MPSR algorithm. Finally, we achieve top-1 accuracy of 88.95% and top-3 accuracy of 98.58% on a dataset containing 9090 training samples and 3232 testing samples for 101 Chemical symbols.

Keywords

handwriting recognition; handwritten character recognition; hidden Markov models; pattern classification; support vector machines; geometrical feature; handwritten chemical symbols; improved SVM-HMM based classifier; nonring structure; online recognition; organic ring structure; statistical feature; Accuracy; Chemicals; Classification algorithms; Feature extraction; Handwriting recognition; Hidden Markov models; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition (CCPR), 2010 Chinese Conference on

Conference_Location

Chongqing

Print_ISBN

978-1-4244-7209-3

Electronic_ISBN

978-1-4244-7210-9

Type

conf

DOI

10.1109/CCPR.2010.5659336

Filename

5659336