We present a study of designing compact multiple-prototype based classifiers for rotation-free recognition of online handwritten Chinese characters. Several versions of Rprop algorithms are adopted to optimize a sample-separation-margin based minimum classification error objective function. Split vector quantization technique is used to compress classifier parameters and a fast-match tree is used for efficient recognition. A new preprocessing technique is proposed to
\nachieve rotation-free recognition capability. Promising benchmark results are reported on an online handwritten character recognition task with a vocabulary of 27,720 characters.<\/p>\n","protected":false},"excerpt":{"rendered":"
We present a study of designing compact multiple-prototype based classifiers for rotation-free recognition of online handwritten Chinese characters. Several versions of Rprop algorithms are adopted to optimize a sample-separation-margin based minimum classification error objective function. Split vector quantization technique is used to compress classifier parameters and a fast-match tree is used for efficient recognition. A 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