  Offline Chinese handwriting text recognition is a long-standing research
topic in the field of pattern recognition. In previous studies, text detection
and recognition are separated, which leads to the fact that text recognition is
highly dependent on the detection results. In this paper, we propose a robust
end-to-end Chinese text page spotter framework. It unifies text detection and
text recognition with text kernel that integrates global text feature
information to optimize the recognition from multiple scales, which reduces the
dependence of detection and improves the robustness of the system. Our method
achieves state-of-the-art results on the CASIA-HWDB2.0-2.2 dataset and
ICDAR-2013 competition dataset. Without any language model, the correct rates
are 99.12% and 94.27% for line-level recognition, and 99.03% and 94.20% for
page-level recognition, respectively.
