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Published in:
2019 | OriginalPaper | Chapter
Applying Deep Convolutional Neural Network to Cursive Chinese Calligraphy Recognition
Authors : Liang Jung, Wen-Hung Liao
Published in: New Trends in Computer Technologies and Applications
Publisher: Springer Singapore
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Abstract
Calligraphy is one of the most important cultural art as well as writing tool in ancient China. Various writing styles have evolved over time in Calligraphy text, including Regular script, Clerical script, Semi-cursive script, Cursive script, and Seal script. In this study, we consider the cursive Chinese calligraphy recognition task as a variant of handwritten text recognition. We apply deep convolutional network approach to this recognition problem and achieve 84.6%, 92.6%, 93.7%, 96.7% average top1, top3, top5, top10 accuracy for 395 characters and 83.8%, 91.8%, 94%, 96.1% average top1, top3, top5, top10 accuracy for 632 characters. Our investigation indicates that text recognition tasks can be tackled by deep learning based approach even only when a limited number of training samples are available.