Confidence-based classifier design
Pattern Recognition
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Online stroke modeling for handwriting recognition
CASCON '08 Proceedings of the 2008 conference of the center for advanced studies on collaborative research: meeting of minds
Online Recognition of Multi-Stroke Symbols with Orthogonal Series
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Polynomial approximation in handwriting recognition
Proceedings of the 2011 International Workshop on Symbolic-Numeric Computation
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Recent work on computer recognition of handwritten mathematical symbols has reached the state where geometric analysis of isolated characters can correctly identify individual characters about 96% of the time. This paper presents confidence measures for two classification methods applied to the recognition of handwritten mathematical symbols. We show how the distance to the nearest convex hull of nearest neighbors relates to the classification accuracy. For multi-classifiers based on support vector machine ensembles, we show how the outcomes of the binary classifiers can be combined into an overall confidence value.