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Autotator: Semi-automatic approach for accelerating the chart image annotation process
Cited 4 time in
Web of Science
Cited 2 time in Scopus
- Authors
- Issue Date
- 2019-11
- Publisher
- Association for Computing Machinery, Inc
- Citation
- ISS 2019 - Proceedings of the 2019 ACM International Conference on Interactive Surfaces and Spaces, pp.315-318
- Abstract
- © 2019 Copyright is held by the owner/author(s).Annotating chart images for training machine learning models is tedious and repetitive especially in that chart images often have a large number of visual elements to annotate. We present Autotator, a semi-automatic chart annotation system that automatically provides suggestions for three annotation tasks such as labeling a chart type, annotating bounding boxes, and associating a quantity. We also present a web-based interface that allows users to interact with the suggestions provided by the system. Finally, we demonstrate a use case of our system where an annotator builds a training corpus of bar charts.
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