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Automatic Extraction of Data from Bar Charts

Authors:

Rabah A. Al-Zaidy,

C. Lee GilesAuthors Info & Claims

K-CAP '15: Proceedings of the 8th International Conference on Knowledge Capture

Article No.: 30, Pages 1 - 4

https://doi.org/10.1145/2815833.2816956

Published: 07 October 2015 Publication History

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Abstract

Scientific charts are an effective tool to visualize numerical data trends. They appear in a wide range of contexts, from experimental results in scientific papers to statistical analyses in business reports. The abundance of scientific charts in the web has made it inevitable for search engines to include them as indexed content. However, the queries based on only the textual data used to tag the images can limit query results. Many studies exist to address the extraction of data from scientific diagrams in order to improve search results. In our approach to achieving this goal, we attempt to enhance the semantic labeling of the charts by using the original data values that these charts were designed to represent. In this paper, we describe a method to extract data values from a specific class of charts, bar charts. The extraction process is fully automated using image processing and text recognition techniques combined with various heuristics derived from the graphical properties of bar charts. The extracted information can be used to enrich the indexing content for bar charts and improve search results. We evaluate the effectiveness of our method on bar charts drawn from the web as well as charts embedded in digital documents.

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Automatic Extraction of Data from Bar Charts

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Published In

K-CAP '15: Proceedings of the 8th International Conference on Knowledge Capture

October 2015

209 pages

ISBN:9781450338493

DOI:10.1145/2815833

Conference Chair:
Ken Barker
IBM Watson Research, USA
,
Program Chair:
José Manuel Gómez-Pérez
Expert System, Spain

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGAI: ACM Special Interest Group on Artificial Intelligence

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Association for Computing Machinery

New York, NY, United States

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Published: 07 October 2015

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K-CAP 2015

K-CAP 2015: Knowledge Capture Conference

October 7 - 10, 2015

NY, Palisades, USA

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K-CAP '15 Paper Acceptance Rate 16 of 56 submissions, 29%;

Overall Acceptance Rate 55 of 198 submissions, 28%

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Huang JChen HYu FLu W(2024)From Detection to Application: Recent Advances in Understanding Scientific Tables and FiguresACM Computing Surveys10.1145/365728556:10(1-39)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3657285
Chundury PReyazuddin YJordan JLazar JElmqvist N(2024)TactualPlot: Spatializing Data as Sound Using Sensory Substitution for Touchscreen AccessibilityIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332693730:1(836-846)Online publication date: 1-Jan-2024
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Shen LShen ELuo YYang XHu XZhang XTai ZWang J(2023)Towards Natural Language Interfaces for Data Visualization: A SurveyIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.314800729:6(3121-3144)Online publication date: 1-Jun-2023
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C SJoshi PA L(2023)Data Extraction and Question Answering on Chart Images Towards Accessibility and Data InterpretationIEEE Open Journal of the Computer Society10.1109/OJCS.2023.33287674(314-325)Online publication date: 2023
https://doi.org/10.1109/OJCS.2023.3328767
Mustafa OAli MMoetesum MSiddiqi I(2023)ChartEye: A Deep Learning Framework for Chart Information Extraction2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA)10.1109/DICTA60407.2023.00082(554-561)Online publication date: 28-Nov-2023
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Bajić FJob J(2023)Review of chart image detection and classificationInternational Journal on Document Analysis and Recognition10.1007/s10032-022-00424-526:4(453-474)Online publication date: 12-Jan-2023
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Bajić FJob J(2022)Data Extraction of Circular-Shaped and Grid-like Chart ImagesJournal of Imaging10.3390/jimaging80501368:5(136)Online publication date: 12-May-2022
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BIRANT DAKÇA ABOZKURT BBAĞLAN M(2022)Classification of Scatter Plot Images Using Deep LearningDeu Muhendislik Fakultesi Fen ve Muhendislik10.21205/deufmd.202224712624:71(631-642)Online publication date: 16-May-2022
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Cui WWang JHuang HWang YLin CZhang HZhang D(2022)A Mixed-Initiative Approach to Reusing Infographic ChartsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.311485628:1(173-183)Online publication date: 1-Jan-2022
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BarChartAnalyzer: Data Extraction and Summarization of Bar Charts from Images

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