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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

Scatteract: Automated Extraction of Data from Scatter Plots

verfasst von : Mathieu Cliche, David Rosenberg, Dhruv Madeka, Connie Yee

Erschienen in: Machine Learning and Knowledge Discovery in Databases

Verlag: Springer International Publishing

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Abstract

Charts are an excellent way to convey patterns and trends in data, but they do not facilitate further modeling of the data or close inspection of individual data points. We present a fully automated system for extracting the numerical values of data points from images of scatter plots. We use deep learning techniques to identify the key components of the chart, and optical character recognition together with robust regression to map from pixels to the coordinate system of the chart. We focus on scatter plots with linear scales, which already have several interesting challenges. Previous work has done fully automatic extraction for other types of charts, but to our knowledge this is the first approach that is fully automatic for scatter plots. Our method performs well, achieving successful data extraction on 89% of the plots in our test set.

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Vorheriges Kapitel Guiding InfoGAN with Semi-supervision
Nächstes Kapitel Unsupervised Diverse Colorization via Generative Adversarial Networks
Fußnoten
2
As is traditional, we refer to the horizontal axis as “X” and the vertical axis as “Y”.
 
4
The procedure is largely inspired by this blog post: http://​felix.​abecassis.​me/​2011/​10/​opencv-rotation-deskewing/​.
 
5
We use the center of the tick-mark bounding box as the tick mark location.
 
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Metadaten
Titel
Scatteract: Automated Extraction of Data from Scatter Plots
verfasst von
Mathieu Cliche
David Rosenberg
Dhruv Madeka
Connie Yee
Copyright-Jahr
2017
Buch
Machine Learning and Knowledge Discovery in Databases

Print ISBN: 978-3-319-71248-2

Electronic ISBN: 978-3-319-71249-9

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-71249-9_9