TY - GEN
T1 - ChartReformer
T2 - 18th International Conference on Document Analysis and Recognition, ICDAR 2024
AU - Yan, Pengyu
AU - Bhosale, Mahesh
AU - Lal, Jay
AU - Adhikari, Bikhyat
AU - Doermann, David
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Chart visualizations are essential for data interpretation and communication; however, most charts are only accessible in image format and lack the corresponding data tables and supplementary information, making it difficult to alter their appearance for different scenarios of application. To eliminate the need for original underlying data and information to perform chart editing, we propose ChartReformer, a natural language-driven chart image editing solution that directly edits the charts from the input images with the given instruction prompts. Instead of predicting the plotting code, the key in this method is that we allow the model to comprehend the chart and reason over the prompt to generate the corresponding underlying data table and visual attributes for new charts, enabling a precise and stable editing result. To generalize ChartReformer, we define and standardize the chart editing category and generate the ChartCraft dataset, covering style, layout, format, and data-centric edits. The experiments show promising results for the natural language-driven chart image editing. Our datasets and model are available at: https://github.com/pengyu965/ChartReformer.
AB - Chart visualizations are essential for data interpretation and communication; however, most charts are only accessible in image format and lack the corresponding data tables and supplementary information, making it difficult to alter their appearance for different scenarios of application. To eliminate the need for original underlying data and information to perform chart editing, we propose ChartReformer, a natural language-driven chart image editing solution that directly edits the charts from the input images with the given instruction prompts. Instead of predicting the plotting code, the key in this method is that we allow the model to comprehend the chart and reason over the prompt to generate the corresponding underlying data table and visual attributes for new charts, enabling a precise and stable editing result. To generalize ChartReformer, we define and standardize the chart editing category and generate the ChartCraft dataset, covering style, layout, format, and data-centric edits. The experiments show promising results for the natural language-driven chart image editing. Our datasets and model are available at: https://github.com/pengyu965/ChartReformer.
KW - Chart Appearance Editing
KW - Chart Data Extraction
KW - Chart Editing
KW - Chart Understanding
KW - Visual Language Model
UR - https://www.scopus.com/pages/publications/85204524840
U2 - 10.1007/978-3-031-70533-5_26
DO - 10.1007/978-3-031-70533-5_26
M3 - Conference contribution
AN - SCOPUS:85204524840
SN - 9783031705328
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 453
EP - 469
BT - Document Analysis and Recognition - ICDAR 2024 - 18th International Conference, Proceedings
A2 - Barney Smith, Elisa H.
A2 - Liwicki, Marcus
A2 - Peng, Liangrui
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 30 August 2024 through 4 September 2024
ER -