TY - GEN
T1 - CHART-Info 2024
T2 - 27th International Conference on Pattern Recognition, ICPR 2024
AU - Davila, Kenny
AU - Lazarus, Rupak
AU - Xu, Fei
AU - Rodríguez Alcántara, Nicole
AU - Setlur, Srirangaraj
AU - Govindaraju, Venu
AU - Mondal, Ajoy
AU - Jawahar, C. V.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Charts are tools for data communication used in a wide range of documents. Recently, the pattern recognition community has shown interest in developing methods for automatically processing charts found in the wild. Following previous efforts on ICPR’s CHART-Infographics competitions, here we propose a newer, larger dataset and benchmark for analyzing and recognizing charts. Inspired by the steps required to make sense of a chart image, the benchmark is divided into 7 different tasks: chart image classification, chart text detection and recognition, text role classification, axis analysis, legend analysis, data extraction, and end-to-end data extraction. We also show the performance of different baselines for the first five tasks. We expect that the increased scale of the proposed dataset will enable the development of better chart recognition systems.
AB - Charts are tools for data communication used in a wide range of documents. Recently, the pattern recognition community has shown interest in developing methods for automatically processing charts found in the wild. Following previous efforts on ICPR’s CHART-Infographics competitions, here we propose a newer, larger dataset and benchmark for analyzing and recognizing charts. Inspired by the steps required to make sense of a chart image, the benchmark is divided into 7 different tasks: chart image classification, chart text detection and recognition, text role classification, axis analysis, legend analysis, data extraction, and end-to-end data extraction. We also show the performance of different baselines for the first five tasks. We expect that the increased scale of the proposed dataset will enable the development of better chart recognition systems.
KW - Charts
KW - Dataset
KW - Graphic Recognition
UR - https://www.scopus.com/pages/publications/85212262892
U2 - 10.1007/978-3-031-78495-8_19
DO - 10.1007/978-3-031-78495-8_19
M3 - Conference contribution
AN - SCOPUS:85212262892
SN - 9783031784941
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 297
EP - 315
BT - Pattern Recognition - 27th International Conference, ICPR 2024, Proceedings
A2 - Antonacopoulos, Apostolos
A2 - Chaudhuri, Subhasis
A2 - Chellappa, Rama
A2 - Liu, Cheng-Lin
A2 - Bhattacharya, Saumik
A2 - Pal, Umapada
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 1 December 2024 through 5 December 2024
ER -