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Confidence measures in recognizing handwritten mathematical symbols. (English) Zbl 1247.68228

Carette, Jacques (ed.) et al., Intelligent computer mathematics. 16th symposium, Calculemus 2009, 8th international conference, MKM 2009, held as part of CICM 2009, Grand Bend, Canada, July 6–12, 2009. Proceedings. Berlin: Springer (ISBN 978-3-642-02613-3/pbk). Lecture Notes in Computer Science 5625. Lecture Notes in Artificial Intelligence, 460-466 (2009).
Summary: 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.
For the entire collection see [Zbl 1165.68005].

MSC:

68T10 Pattern recognition, speech recognition
68T05 Learning and adaptive systems in artificial intelligence
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[1] Char, B., Watt, S.M.: Representing and Characterizing Handwritten Mathematical Symbols through Succinct Functional Approximation. In: Proc. Intl. Conf. on Docum. Anal. and Rec. (ICDAR), pp. 1198–1202 (2007)
[2] Golubitsky, O., Watt, S.M.: Online Stroke Modeling for Handwriting Recognition. In: Proc. 18th Intl. Conf. on Comp. Sci. and Soft. Eng. (CASCON), pp. 72–80 (2008)
[3] Golubitsky, O., Watt, S.M.: Online Computation of Similarity between Handwritten Characters. In: Proc. Document Recognition and Retrieval (DRR XVI), pp. C1–C10 (2009)
[4] Golubitsky, O., Watt, S.M.: Online Recognition of Multi-Stroke Symbols with Orthogonal Series. In: ICDAR (accepted, 2009)
[5] Golubitsky, O., Watt, S.M.: Improved Character Recognition through Subclassing and Runoff Elections. Ontario Research Center for Computer Algebra Tecnical Report TR-09-01
[6] Golubitsky, O., Watt, S.M.: Tie Breaking for Curve Multiclassifiers. Ontario Research Center for Computer Algebra Tecnical Report TR-09-02
[7] Watt, S.M.: Mathematical Document Classification via Symbol Frequency Analysis. In: Proc. Towards Digital Mathematics Library (DML 2008), pp. 29–40 (2008) · Zbl 1170.68494
[8] Li, M., Sethi, I.: Confidence-Based Classifier Design. Pattern Recognition 39(7), 1230–1240 (2006) · Zbl 1095.68666
[9] Vincent, P., Bengio, Y.: K-local Hyperplane and Convex Distance Nearest Neighbor Algorithms. In: Adv. in Neural Inform. Proc. Systems, pp. 985–992. MIT Press, Cambridge (2002)
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