ICDAR 2019 Competition on Harvesting Raw Tables from Infographics (CHART-Infographics)

• Main Authors: Davila, Kenny, Kota, Bhargava Urala, Setlur, Srirangaraj, Govindaraju, Venu, Tensmeyer, Christopher, Shekhar, Sumit, Chaudhry, Ritwick
• Format: Conference Proceeding
• Language: English
• Subjects: Bars; Chart-Recognition; Data mining; Document-Analysis; Graphics-Recognition; Measurement; Performance-Evaluation; Task analysis; Text recognition; Text-Recognition-and-Classification; Training
• ISSN: 2379-2140
• DOI: 10.1109/ICDAR.2019.00203

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.