VQ-Based On-Line Handwritten Character Recognition through Learning and Adaptive Edit Distances

Abstract : In this paper, we study the application of two forms of edit distance (ED) in an on-line handwritten character recognition system which is based on vector quantization techniques (VQ). A learning ED and an adaptive ED are proposed respectively for tasks of codebook generation and character recognition. Here, the cost functions are constructed on the modelling precision of handwriting primitives. For the learning ED, the cost function is static and derived by evaluating the primitives globally on the whole training database. For the adaptive ED, the cost function becomes dynamic and is adapted to the modelling errors at each time instant. The built system is tested on an online handwritten character recognition application on the UNIPEN data corpus.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01561387
Contributor : Lip6 Publications <>
Submitted on : Wednesday, July 12, 2017 - 4:42:58 PM
Last modification on : Thursday, March 21, 2019 - 1:03:50 PM

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Haifeng Li, Thierry Artières, Patrick Gallinari, Bernadette Dorizzi. VQ-Based On-Line Handwritten Character Recognition through Learning and Adaptive Edit Distances. ICONIP 2002 - 9th International Conference on Neural Information Processing, Nov 2002, Singapore, Singapore. pp.2008-2012, ⟨10.1109/ICONIP.2002.1199025⟩. ⟨hal-01561387⟩

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