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DenseRAN for Offline Handwritten Chinese Character Recognition by Wang, Wenchao; Zhang, Jianshu; Du, Jun; Wang, Zi-Rui; Zhu, Yixing is a scholarly article available to read on EtoBox.

What is DenseRAN for Offline Handwritten Chinese Character Recognition about?

Recently, great success has been achieved in offline handwritten Chinese character recognition by using deep learning methods. Chinese characters are mainly logographic and consist of basic radicals, however, previous research mostly treated each Chinese character as a whole without explicitly considering its internal two-dimensional structure and radicals. In this study, we propose a novel radical analysis network with densely connected architecture (DenseRAN) to analyze Chinese character radicals and its two-dimensional structures simultaneously. DenseRAN first encodes input image to high-level visual features by employing DenseNet as an encoder. Then a decoder based on recurrent neural networks is employed, aiming at generating captions of Chinese characters by detecting radicals and two-dimensional structures through attention mechanism. The manner of treating a Chinese character as a composition of two-dimensional structures and radicals can reduce the size of vocabulary and enable DenseRAN to possess the capability of recognizing unseen Chinese character classes, only if the corresponding radicals have been seen in training set. Evaluated on ICDAR-2013 competition database, t

Author

Wang, Wenchao; Zhang, Jianshu; Du, Jun; Wang, Zi-Rui; Zhu, Yixing

Published

2018

Language

EN