Massive character recognition with a large ground-truthed database

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In character recognition, multiple prototype classifiers, where multiple patterns are prepared as representative patterns of each class, have often been employed to improve recognition accuracy. Our question is how we can improve the recognition accuracy by increasing prototypes massively in the multiple prototype classifier. In this paper, we will answer this question through several experimental analyses, using a simple 1-nearest neighbor (1-NN) classifier and about 550,000 manually labeled handwritten numeral patterns. The analysis results under the leave-one-out evaluation showed not only a simple fact that more prototypes provide fewer recognition errors, but also a more important fact that the error rate decreases approximately to 40% by increasing the prototypes 10 times. The analysis results also showed other phenomena in massive character recognition, such that the NN prototypes become visually closer to the input pattern by increasing the prototypes.

Original languageEnglish
Title of host publication26th Annual ACM Symposium on Applied Computing, SAC 2011
Number of pages5
Publication statusPublished - Jun 23 2011
Event26th Annual ACM Symposium on Applied Computing, SAC 2011 - TaiChung, Taiwan, Province of China
Duration: Mar 21 2011Mar 24 2011

Publication series

NameProceedings of the ACM Symposium on Applied Computing


Other26th Annual ACM Symposium on Applied Computing, SAC 2011
Country/TerritoryTaiwan, Province of China

All Science Journal Classification (ASJC) codes

  • Software


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