Automatic extraction of data from 2-D plots in documents

Xiaonan Lu, James Z. Wang, Prasenjit Mitra, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

Two-dimensional (2-D) plots in digital documents contain important information. Often, the results of scientific experiments and performance of businesses are summarized using plots. Although 2-D plots are easily understood by human users, current search engines rarely utilize the information contained in the plots to enhance the results returned in response to queries posed by endusers. We propose an automated algorithm for extracting information from line curves in 2-D plots. The extracted information can be stored in a database and indexed to answer end-user queries and enhance search results. We have collected 2-D plot images from a variety of resources and tested our extraction algorithms. Experimental evaluation has demonstrated that our method can produce results suitable for real world use.

Original languageEnglish
Title of host publicationProceedings - 9th International Conference on Document Analysis and Recognition, ICDAR 2007
Pages188-192
Number of pages5
DOIs
Publication statusPublished - 1 Dec 2007
Event9th International Conference on Document Analysis and Recognition, ICDAR 2007 - Curitiba, Brazil
Duration: 23 Sep 200726 Sep 2007

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
Volume1
ISSN (Print)1520-5363

Conference

Conference9th International Conference on Document Analysis and Recognition, ICDAR 2007
CountryBrazil
CityCuritiba
Period23/9/0726/9/07

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Lu, X., Wang, J. Z., Mitra, P., & Giles, C. L. (2007). Automatic extraction of data from 2-D plots in documents. In Proceedings - 9th International Conference on Document Analysis and Recognition, ICDAR 2007 (pp. 188-192). [4378701] (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR; Vol. 1). https://doi.org/10.1109/ICDAR.2007.4378701