Approaches and Strategies for Digital Construction of Archive Resources Based on Cloud Storage
- DOI
- 10.2991/978-94-6463-326-9_30How to use a DOI?
- Keywords
- Cloud storage; Archives; Digitization; Administration
- Abstract
In order to solve the technical problem of incomplete information extraction and lack of effective compensation in the digital transformation process of existing archives, which leads to poor digital management of archives, the preset area is scanned to obtain the first OCR scanned file. Perform template matching and generate file matching templates. Compare the file matching template with the first OCR scan file to obtain missing attribute information and the distribution location of missing attributes. Perform local compensation scanning to generate a second OCR scanning file. Conduct secondary retrieval to generate digital archive retrieval results. Adjust the timing of the second OCR scan file and digital archive retrieval results, generate archive classification results, and update the cloud storage repository. It can achieve the technical effect of improving the integrity of archive information extraction, achieving automatic compensation, and thus enhancing the effectiveness of archive digital management.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Qi Yang AU - Jinghuan Zhu AU - Shilong Wang PY - 2023 DA - 2023/12/30 TI - Approaches and Strategies for Digital Construction of Archive Resources Based on Cloud Storage BT - Proceedings of the 2023 3rd International Conference on Business Administration and Data Science (BADS 2023) PB - Atlantis Press SP - 281 EP - 288 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-326-9_30 DO - 10.2991/978-94-6463-326-9_30 ID - Yang2023 ER -