This work summarizes the results of the first
Competition on Harvesting Raw Tables from Infographics
(ICDAR 2019 CHART-Infographics). The complex process of
automatic chart recognition is divided into multiple tasks
for the purpose of this competition, including Chart Image
Classification (Task 1), Text Detection and Recognition (Task 2),
Text Role Classification (Task 3), Axis Analysis (Task 4), Legend
Analysis (Task 5), Plot Element Detection and Classification
(Task 6.a), Data Extraction (Task 6.b), and End-to-End Data
Extraction (Task 7). We provided a large synthetic training set
and evaluated submitted systems using newly proposed metrics
on both synthetic charts and manually-annotated real charts
taken from scientific literature. A total of 8 groups registered
for the competition out of which 5 submitted results for tasks
1-5. The results show that some tasks can be performed highly
accurately on synthetic data, but all systems did not perform
as well on real world charts. The data, annotation tools, and
evaluation scripts have been publicly released for academic use.