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International Journal on Document Analysis and Recognition (IJDAR)

Erschienen in:

01.12.2013 | Original Paper

Fast self-generation voting for handwritten Chinese character recognition

verfasst von: Yunxue Shao, Chunheng Wang, Baihua Xiao

Erschienen in: International Journal on Document Analysis and Recognition (IJDAR) | Ausgabe 4/2013

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Abstract

In this paper, a fast self-generation voting method is proposed for further improving the performance in handwritten Chinese character recognition. In this method, firstly, a set of samples are generated by the proposed fast self-generation method, and then these samples are classified by the baseline classifier, and the final recognition result is determined by voting from these classification results. Two methods that are normalization-cooperated feature extraction strategy and an approximated line density are used for speeding up the self-generation method. We evaluate the proposed method on the CASIA and CASIA-HWDB1.1 databases. High recognition rate of 98.84 % on the CASIA database and 91.17 % on the CASIA-HWDB1.1 database are obtained. These results demonstrate that the proposed method outperforms the state-of-the-art methods and is useful for practical applications.

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Titel: Fast self-generation voting for handwritten Chinese character recognition
verfasst von: Yunxue Shao
Chunheng Wang
Baihua Xiao
Publikationsdatum: 01.12.2013
Verlag: Springer Berlin Heidelberg
Erschienen in: International Journal on Document Analysis and Recognition (IJDAR) / Ausgabe 4/2013
Print ISSN: 1433-2833
Elektronische ISSN: 1433-2825
DOI: https://doi.org/10.1007/s10032-012-0194-8

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