Autotator: Semi-Automatic Approach for Accelerating the Chart Image Annotation Process
Junhoe Kim, Jaemin Jo, Jinwook Seo · 2019
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.