TY - GEN
T1 - ICDAR 2019 competition on harvesting raw tables from infographics (CHART-infographics)
AU - Davila, Kenny
AU - Kota, Bhargava Urala
AU - Setlur, Srirangaraj
AU - Govindaraju, Venu
AU - Tensmeyer, Christopher
AU - Shekhar, Sumit
AU - Chaudhry, Ritwick
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - 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.
AB - 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.
KW - Chart-Recognition
KW - Document-Analysis
KW - Graphics-Recognition
KW - Performance-Evaluation
KW - Text-Recognition-and-Classification
UR - https://www.scopus.com/pages/publications/85079844826
U2 - 10.1109/ICDAR.2019.00203
DO - 10.1109/ICDAR.2019.00203
M3 - Conference contribution
AN - SCOPUS:85079844826
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 1594
EP - 1599
BT - Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019
PB - IEEE Computer Society
T2 - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019
Y2 - 20 September 2019 through 25 September 2019
ER -