A SVM-HMM Based Online Classifier for Handwritten Chemical Symbols
Pattern Recognition（2010）
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.
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Key words
recognition accuracy,handwritten chemical symbols recognition,test data set,hmm method,test sample,test data,handwritten chemical symbols,top-1 accuracy,online classifier,top-3 accuracy,ors symbol,fine recognition,support vector machines,chemicals,hidden markov models,accuracy,handwriting recognition,kernel,image classification,classification algorithms
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