Please wait a minute...
New Technology of Library and Information Service  2003, Vol. 19 Issue (1): 22-24    DOI: 10.11925/infotech.1003-3513.2003.01.08
Current Issue | Archive | Adv Search |
Options for Digitization and Compression of Document Images
Li Xing
(Information Center, China National Institute of Cultural Property,Beijing 100029,China)
Download:
Export: BibTeX | EndNote (RIS)      
Abstract  

Digitization and compression of document images is the new option for preserving documents in libraries and archival institutions. It is the prerequisite for on-line service of information through Internet and is a general trend of the development of libraries and archival institutions throughout the world. This paper introduces the basic concepts and principles of digitizing document images and latest achievements in some of the libraries abroad. By comparing the characteristics of the most popular image compression standards currently used, this paper gives detailed information of JPEG 2000, the most promising still image compressing standard and its application in library's practices in the world.

Key wordsDocument      Image      Digitization      Compression      JPEG 2000     
Received: 11 July 2002      Published: 25 February 2003
ZTFLH: 

TP317

 
Corresponding Authors: Li Xing   
About author:: Li Xing

Cite this article:

Li Xing. Options for Digitization and Compression of Document Images. New Technology of Library and Information Service, 2003, 19(1): 22-24.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2003.01.08     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2003/V19/I1/22

[1] Louis H.Sharpe“JPEG2000 Options for Document Image Compression”, http://www.loc.gov/preserv/4670-18.pdf
[2] Rich Entlich & Oya Y.Rieger "Cornell University Library: Preserving Cornell's Digital Image Collections: Implementing an Archival Strategy" , http://www.library.cornell.edu/imls/index.htm
[3] Diego Santa-Cruz & Touradj Ebrahimi“JPEG 2000 功能的分析研究”, http://www.jpeg.org/public/wglu1815.pdf
[4] ARL,“Electronic Reserves Operations in ARL Library”, http://www.arl.org/spec/245fly.html, May 1999
[5] ARL,“Digitizing Technologies for Preservation”, http://www.arl.org/spec/214fly.html, March 1996

 

[1] Xie Hao,Mao Jin,Li Gang. Sentiment Classification of Image-Text Information with Multi-Layer Semantic Fusion[J]. 数据分析与知识发现, 2021, 5(6): 103-114.
[2] Wang Yizhen,Ou Shiyan,Chen Jinju. Automatic Abstracting Civil Judgment Documents with Two-Stage Procedure[J]. 数据分析与知识发现, 2021, 5(5): 104-114.
[3] Xu Zheng,Le Xiaoqiu. Generating AND-OR Logical Expressions for Semantic Features of Categorical Documents[J]. 数据分析与知识发现, 2021, 5(5): 95-103.
[4] Xu Guang,Ren Ming,Song Chengyu. Extracting China’s Economic Image from Western News[J]. 数据分析与知识发现, 2021, 5(5): 30-40.
[5] Liu Huan,Zhang Zhixiong,Wang Yufei. A Review on Main Optimization Methods of BERT[J]. 数据分析与知识发现, 2021, 5(1): 3-15.
[6] Xinyu Zai,Xuedong Tian. Retrieving Scientific Documents with Formula Description Structure and Word Embedding[J]. 数据分析与知识发现, 2020, 4(1): 131-138.
[7] Qingmin Liu,Changqing Yao,Chongde Shi,Xiaojie Wen,Yueying Sun. Vocabulary Optimization of Neural Machine Translation for Scientific and Technical Document[J]. 数据分析与知识发现, 2019, 3(3): 76-82.
[8] Lu Wei,Luo Mengqi,Ding Heng,Li Xin. Image Annotation Tags by Deep Learning and Real Users: A Comparative Study[J]. 数据分析与知识发现, 2018, 2(5): 1-10.
[9] Wang Xueying,Wang Hao,Zhang Zixuan. Recognizing Semantics of Continuous Strings in Chinese Patent Documents[J]. 数据分析与知识发现, 2018, 2(5): 11-22.
[10] Li Lin,Li Hui. Computing Text Similarity Based on Concept Vector Space[J]. 数据分析与知识发现, 2018, 2(5): 48-58.
[11] Liu Dongsu,Huo Chenhui. Recommending Image Based on Feature Matching[J]. 数据分析与知识发现, 2018, 2(3): 49-59.
[12] Xu Jianmin,Xu Caiyun. Computing Similarity of Sci-Tech Documents Based on Texts and Formulas[J]. 数据分析与知识发现, 2018, 2(10): 103-109.
[13] Jia Shanshan,Liu Chang,Sun Lianying,Liu Xiaoan,Peng Tao. Patent Classification Based on Multi-feature and Multi-classifier Integration[J]. 数据分析与知识发现, 2017, 1(8): 76-84.
[14] Zeng Jin,Lu Wei,Ding Heng,Chen Haihua. Modeling User’s Interests Based on Image Semantics[J]. 数据分析与知识发现, 2017, 1(4): 76-83.
[15] Zhang Xiaoojuan. Analyzing Dynamic Informational, Navigational and Transactional Online Queries[J]. 数据分析与知识发现, 2017, 1(4): 9-19.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn