HRCenterNet: An Anchorless Approach to Chinese Character Segmentation in Historical Documents
This research provides a more accurate and efficient method for character segmentation in historical Chinese documents, which is crucial for the automatic digitization and preservation of these valuable cultural artifacts.
This paper addresses the challenge of segmenting Chinese characters in historical documents, proposing HRCenterNet, an anchorless object detection model with a parallelized architecture. The model achieves an average IoU of 0.81 on the MTHv2 dataset, demonstrating a superior speed-accuracy trade-off compared to existing methods.
The information provided by historical documents has always been indispensable in the transmission of human civilization, but it has also made these books susceptible to damage due to various factors.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 The MTHv2 dataset consists of over 3000 Chinese historical document images and over 1 million individual Chinese characters; with these enormous data, the segmentation capability of our model achieves IoU 0.81 on average with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.