Radical Region Based CNN for Offline Handwritten Chinese Character ...
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Experiment results show the proposed methods improve recognition accuracy of current models. The performance of the best model has been raised to 97.42% on ...
For offline HCCR, the main target is to recognize Chinese characters in gray-scaled images. In the past fifty years, various methods have been proposed to ...
Experiment results show the proposed methods improve recognition accuracy of current models. The performance of the best model has been raised to 97.42% on ...
Bibliographic details on Radical Region Based CNN for Offline Handwritten Chinese Character Recognition.
East-Asian characters possess a rich hierarchical structure with each character comprising a unique spatial arrangement of radicals (sub-characters).
This paper reviews the research progress and challenges of offline handwritten Chinese recognition based on traditional techniques, deep learning methods.
Radical Region Based CNN for Offline Handwritten Chinese Character Recognition · Luo WeikeK. Sei-ichiro. Computer Science. 2017 4th IAPR Asian Conference on ...
As different Chinese characters share some common radicals and structures, our method is able to recognize new categories without any labeled samples from them.
In this study, we propose a novel radical analysis network with densely connected architecture (DenseRAN) to analyze Chinese character radicals and its two- ...
Aug 4, 2019 · In this paper, a novel radical aggregation network (RAN) is proposed for few-shot/zero-shot offline handwritten Chinese character recognition.
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