Online Character Recognition Based on Elastic Matching and Quadratic Discrimination

We try to link elastic matching with a statistical discrimination framework to overcome the overfitting problem which often degrades the performance of elastic matchingbased online character recognizers. In the proposed technique, elastic matching is used just as an extractor of a feature vector representing the difference between input and reference patterns. Then quadratic discrimination is performed under the assumption that the feature vector is governed by a Gaussian distribution. The result of a recognition experiment on UNIPEN database (Train-R01/V07, 1a) showed that the proposed technique can attain a high recognition rate (97.95%) and outperforms a recent elastic matching-based recognizer.

• Type: Conference
• Year: 2005
• Where: ICDAR
• Authors: Hiroto Mitoma, Seiichi Uchida, Hiroaki Sakoe
• Keywords: Document Analysis, Elastic Matching, Feature Vector, ICDAR 2005, Statistical Discrimination Framework