@inproceedings{949a39d2242442e19a4cb4d5fcdbe05a,
title = "Autotator: Semi-automatic approach for accelerating the chart image annotation process",
abstract = "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.",
keywords = "Chart annotation, Data collection, Deep learning, Information extraction, Mixed-initiative interaction",
author = "Junhoe Kim and Jinwook Seo and Jaemin Jo",
note = "Publisher Copyright: {\textcopyright} 2019 Copyright is held by the owner/author(s).; 14th ACM International Conference on Interactive Surfaces and Spaces, ISS 2019 ; Conference date: 10-11-2019 Through 13-11-2019",
year = "2019",
month = nov,
day = "10",
doi = "10.1145/3343055.3360741",
language = "English",
series = "ISS 2019 - Proceedings of the 2019 ACM International Conference on Interactive Surfaces and Spaces",
publisher = "Association for Computing Machinery, Inc",
pages = "315--318",
booktitle = "ISS 2019 - Proceedings of the 2019 ACM International Conference on Interactive Surfaces and Spaces",
}