A SVM-HMM Based Online Classifier for Handwritten Chemical Symbols

Author

Zhang, Yang ; Shi, Guangshun ; Wang, Kai

Author_Institution

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

fYear

2010

fDate

23-26 Aug. 2010

Firstpage

1888

Lastpage

1891

Abstract

This paper presents a novel double-stage classifier for handwritten chemical symbols recognition task. The first stage is rough classification, SVM method is used to distinguish non-ring structure (NRS) and organic ring structure (ORS) symbols, while HMM method is used for fine recognition at second stage. A point-sequence-reordering algorithm is proposed to improve the recognition accuracy of ORS symbols. Our test data set contains 101 chemical symbols, 9090 training samples and 3232 test samples. Finally, we obtained top-1 accuracy of 93.10% and top-3 accuracy of 98.08% based on the test data set.

Keywords

handwritten character recognition; hidden Markov models; image classification; support vector machines; SVM-HMM; double-stage classifier; handwritten chemical symbols recognition task; nonring structure; online classifier; organic ring structure symbols; point-sequence-reordering algorithm; rough classification; Accuracy; Chemicals; Classification algorithms; Handwriting recognition; Hidden Markov models; Kernel; Support vector machines; double-stage classifier; handwritten chemical symbols; online recognition; stroke-order independent algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition (ICPR), 2010 20th International Conference on

Conference_Location

Istanbul

ISSN

1051-4651

Print_ISBN

978-1-4244-7542-1

Type

conf

DOI

10.1109/ICPR.2010.465

Filename

5597225