TY - GEN
T1 - ICPR 2022
T2 - 26th International Conference on Pattern Recognition, ICPR 2022
AU - Davila, Kenny
AU - Xu, Fei
AU - Ahmed, Saleem
AU - Mendoza, David A.
AU - Setlur, Srirangaraj
AU - Govindaraju, Venu
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The outcomes of the third Challenge on HArvesting Raw Tables from Infographics (ICPR 2022 CHART-Infographics) are presented in this work. Recognizing charts is a difficult process which we divided into the following task: 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 have provided a novel dataset for training reusing all available data from previous challenges, and we also provide a brand new testing dataset for the evaluation of submissions. Both datasets were constructed by manually annotating charts extracted from the Open Access section of the PubMed Central. A total of 9 teams registered out of which 5 submitted results for different tasks of the challenge. Many submissions are based on state-of-the-art methods from computer vision, but the final scores imply that more work will be required to solve the chart recognition problem. The data, annotation tools, and evaluation scripts have been publicly released for academic use.
AB - The outcomes of the third Challenge on HArvesting Raw Tables from Infographics (ICPR 2022 CHART-Infographics) are presented in this work. Recognizing charts is a difficult process which we divided into the following task: 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 have provided a novel dataset for training reusing all available data from previous challenges, and we also provide a brand new testing dataset for the evaluation of submissions. Both datasets were constructed by manually annotating charts extracted from the Open Access section of the PubMed Central. A total of 9 teams registered out of which 5 submitted results for different tasks of the challenge. Many submissions are based on state-of-the-art methods from computer vision, but the final scores imply that more work will be required to solve the chart recognition problem. The data, annotation tools, and evaluation scripts have been publicly released for academic use.
UR - https://www.scopus.com/pages/publications/85143582012
U2 - 10.1109/ICPR56361.2022.9956289
DO - 10.1109/ICPR56361.2022.9956289
M3 - Conference contribution
AN - SCOPUS:85143582012
T3 - Proceedings - International Conference on Pattern Recognition
SP - 4995
EP - 5001
BT - 2022 26th International Conference on Pattern Recognition, ICPR 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 21 August 2022 through 25 August 2022
ER -