  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, the proposed
approach significantly outperforms whole-character modeling approach with a
relative character error rate (CER) reduction of 18.54%. Meanwhile, for the
case of recognizing 3277 unseen Chinese characters in CASIA-HWDB1.2 database,
DenseRAN can achieve a character accuracy of about 41% while the traditional
whole-character method has no capability to handle them.
