  The information provided by historical documents has always been
indispensable in the transmission of human civilization, but it has also made
these books susceptible to damage due to various factors. Thanks to recent
technology, the automatic digitization of these documents are one of the
quickest and most effective means of preservation. The main steps of automatic
text digitization can be divided into two stages, mainly: character
segmentation and character recognition, where the recognition results depend
largely on the accuracy of segmentation. Therefore, in this study, we will only
focus on the character segmentation of historical Chinese documents. In this
research, we propose a model named HRCenterNet, which is combined with an
anchorless object detection method and parallelized architecture. The MTHv2
dataset consists of over 3000 Chinese historical document images and over 1
million individual Chinese characters; with these enormous data, the
segmentation capability of our model achieves IoU 0.81 on average with the best
speed-accuracy trade-off compared to the others. Our source code is available
at https://github.com/Tverous/HRCenterNet.
