Chinese rubbing document image binarization image based on automatic seed region growth
Chaofan Yu, Jiasi Sun, Jialei Chen, Yuhao Liu, Zhi-Kai Huang, Huan Wang · International Conference on Electronic Information Technology (EIT 2022) · 2022
Rubbings of China is a great treasure, of which the recorded information is later generations research was one of the important ways in politics, economy and culture. But the rubbings are vulnerable to damage. However, the partition of rubbings can make this information better preserved, which is of great significance to the protection of cultural relics, historical research and cultural inheritance. This paper combined region growing segmentation and connected domain labeling to segment the historical document image. Firstly, the OTSU algorithm has been employed for document image binarization. Second, the binarized image has been corroded to remove isolated noise points, and then the effective connected area is extracted by the connectivity domain marking algorithm to find the initial value seed point. Finally, the seed point segmented the area growth to obtain a clear binarized document image. Experiments showed that the automatic seed region growth leads to performance improvements over the K-means cluster and OSTU method. We tested our method using the DIBCO 2010 dataset, which showed segmentation accuracy of up to 98%.