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Proceedings Paper

Hybrid SVM-HMM based recognition algorithm for pen-based tutoring system
Author(s): Zhenming Yuan; Hong Pan
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Paper Abstract

Pen-based computing takes advantage of human skill with the pen, which is more than a substitute for the mouse. A hybrid SVM-HMM based recognition algorithm is presented for pen-based single stroke diagram. The algorithm includes five steps: sampling and pre-processing, segmentation, formal feature computing, SVM based feature classification, and HMM based symbol recognition. The formal feature of a stroke is composed of five static features and one dynamic feature. A group of one-to-one combinations of binary SVMs are used as feature classifiers to produce fixed length feature vectors, each of which is produced by the probability output with Sigmoid function and act as the posterior probability of observation of HMM. Finally HMMs are employed as final recognizer to recognize the unknown stroke. Based on this algorithm, a tutoring system is designed to identify the sketches of the flowchart diagrams. Experiment results show the hybrid algorithm has a good learning and recognition ability, which is benefited from combining the SVM's classification ability of static properties with the HMM's recognition ability of dynamic properties.

Paper Details

Date Published: 14 November 2007
PDF: 7 pages
Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 679058 (14 November 2007); doi: 10.1117/12.774813
Show Author Affiliations
Zhenming Yuan, Hangzhou Normal Univ. (China)
Hong Pan, Hangzhou Normal Univ. (China)


Published in SPIE Proceedings Vol. 6790:
MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications

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