ChartDetective: Easy and Accurate Interactive Data Extraction from Complex Vector Charts

Masson, Damien; Malacria, Sylvain; Vogel, Daniel; Lank, Edward; Casiez, Géry

Document type :

Communication dans un congrès avec actes

DOI :

10.1145/3544548.3581113

Title :

ChartDetective: Easy and Accurate Interactive Data Extraction from Complex Vector Charts

Author(s) :

Masson, Damien [Auteur]
Cheriton School of Computer Science [Waterloo] [CS]
Malacria, Sylvain [Auteur]

Technology and knowledge for interaction [LOKI]
Vogel, Daniel [Auteur]
Cheriton School of Computer Science [Waterloo] [CS]
Lank, Edward [Auteur]
Technology and knowledge for interaction [LOKI]
Cheriton School of Computer Science [Waterloo] [CS]
Casiez, Géry [Auteur]

Technology and knowledge for interaction [LOKI]

Conference title :

CHI 2023 - ACM Conference on Human Factors in Computing Systems

City :

Hamburg

Country :

Allemagne

Start date of the conference :

2023-04-22

Book title :

Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2023)

English keyword(s) :

data extraction
chart reverse-engineering
vector graphics

HAL domain(s) :

Informatique [cs]/Interface homme-machine [cs.HC]

English abstract : [en]

Extracting underlying data from rasterized charts is tedious and inaccurate; values might be partially occluded or hard to distinguish, and the quality of the image limits the precision of the data being recovered. To ...
Show more >Extracting underlying data from rasterized charts is tedious and inaccurate; values might be partially occluded or hard to distinguish, and the quality of the image limits the precision of the data being recovered. To address these issues, we introduce a semi-automatic system leveraging vector charts to extract the underlying data easily and accurately. The system is designed to make the most of vector information by relying on a drag-and-drop interface combined with selection, filtering, and previsualization features. A user study showed that participants spent less than 4 minutes to accurately recover data from charts published at CHI with diverse styles, thousands of data points, a combination of different encodings, and elements partially or completely occluded. Compared to other approaches relying on raster images, our tool successfully recovered all data, even when hidden, with a 78% lower relative error.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Promouvoir et améliorer la découverte des fonctionnalités et des interactions dans les systèmes interactifs

Collections :