Development and evaluation of a hierarchical clustering and pattern averaging method applicable to on-line handwritten character recognition based on feature point approximation
| Autor: | Soushiro Kuzunuki, Toshimi Yokota, Keiko Gunji, Nagaharu Hamada, Koyo Katsura |
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| Rok vydání: | 2005 |
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| Zdroj: | Electronics and Communications in Japan (Part III: Fundamental Electronic Science). 88:58-68 |
| ISSN: | 1520-6440 1042-0967 |
| Popis: | With the approximate matching methods based on feature points that have become widely used in on-line character recognition in recent years, it is difficult to collect statistics for individual points as the number of points used to match a single character varies from pattern to pattern. It has therefore not been possible to apply simultaneous clustering and pattern averaging techniques that have proven effective elsewhere in improving recognition rates while controlling the size of a recognition dictionary. In this paper, to deal with this situation, we note that it is possible to use the matching function of a recognition engine to find the average value between two patterns. We propose a method based on hierarchical clustering that allows the simultaneous clustering and averaging of patterns to create a dictionary and is also applicable to on-line character recognition using feature point approximation methods. We have conducted experiments using the handwriting database of Tokyo University of Agriculture and Technology. In these experiments using samples of handwriting from 20 people, we found that by optimizing a cluster merge condition threshold parameter Lth and a recording condition threshold parameter N we were able to obtain good recognition rates: 88.1% for non-kanji characters, 97.6% for 500 kanji characters with multiple dictionary entries, and 91.1% for 500 single entry kanji characters. © 2005 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 88(12): 58–68, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.20209 |
| Databáze: | OpenAIRE |
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