Title :
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
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;
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4244-7542-1
DOI :
10.1109/ICPR.2010.465
