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This work summarizes the results of the first Competition on Harvesting Raw Tables from Infographics (ICDAR 2019 CHART-Infographics). The complex process of automatic chart recognition is divided into multiple tasks for the purpose of this competition, including Chart Image Classification (Task 1), Text Detection and Recognition (Task 2), Text Role Classification (Task 3), Axis Analysis (Task 4), Legend Analysis (Task 5), Plot Element Detection and Classification (Task 6.a), Data Extraction (Task 6.b), and End-to-End Data Extraction (Task 7). We provided a large synthetic training set and evaluated submitted systems using newly proposed metrics on both synthetic charts and manually-annotated real charts taken from scientific literature. A total of 8 groups registered for the competition out of which 5 submitted results for tasks 1-5. The results show that some tasks can be performed highly accurately on synthetic data, but all systems did not perform as well on real world charts. The data, annotation tools, and evaluation scripts have been publicly released for academic use.
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Chart Mining: A Survey of Methods for Automated Chart Analysis
Charts are useful communication tools for the presentation of data in a visually appealing format that facilitates comprehension. There have been many studies dedicated to chart mining, which refers to the process of automatic detection, extraction and analysis of charts to reproduce the tabular data that was originally used to create them. By allowing access to data which might not be available in other formats, chart mining facilitates the creation of many downstream applications. This paper presents a comprehensive survey of approaches across all components of the automated chart mining pipeline such as (i) automated extraction of charts from documents; (ii) processing of multi-panel charts; (iii) automatic image classifiers to collect chart images at scale; (iv) automated extraction of data from each chart image, for popular chart types as well as selected specialized classes; (v) applications of chart mining; and (vi) datasets for training and evaluation, and the methods that were used to build them. Finally, we summarize the main trends found in the literature and provide pointers to areas for further research in chart mining.
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- Award ID(s):
- 1640867
- PAR ID:
- 10292305
- Date Published:
- Journal Name:
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- ISSN:
- 0162-8828
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TPAMI.2020.2992028
