Abstract
Chinese ancient inscriptions have a long history, while natural erosion and human destruction have led to many incomplete inscriptions with low-quality textual data and blurry images. With deep learning technologies, it is expected to use relevant image and language processing tasks to restore inscriptions. To improve the efficiency of restoration tasks and promote the digital protection of cultural heritage, this study used deep learning technology to restore ancient Chinese inscriptions. We combined natural language processing and computer vision technologies to train models for restoring inscriptions. The results indicated that the joint solution had advantages over every single model for incomplete character restoration.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
1 Liaoning Normal University, Digital Protection and Utilisation Laboratory of Historical and Cultural Heritage, Dalian, China (GRID:grid.440818.1) (ISNI:0000 0000 8664 1765)
2 Liaoning Normal University, School of History and Culture, Dalian, China (GRID:grid.440818.1) (ISNI:0000 0000 8664 1765)