[2603.27064] ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding

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Computer Science > Computer Vision and Pattern Recognition

arXiv:2603.27064 (cs)

[Submitted on 28 Mar 2026 (v1), last revised 14 Apr 2026 (this version, v2)]

Title:ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding

Authors:Jovana Kondic, Pengyuan Li, Dhiraj Joshi, Isaac Sanchez, Ben Wiesel, Shafiq Abedin, Amit Alfassy, Eli Schwartz, Daniel Caraballo, Yagmur Gizem Cinar, Florian Scheidegger, Steven I. Ross, Daniel Karl I. Weidele, Hang Hua, Ekaterina Arutyunova, Roei Herzig, Zexue He, Zihan Wang, Xinyue Yu, Yunfei Zhao, Sicong Jiang, Minghao Liu, Qunshu Lin, Peter Staar, Luis Lastras, Aude Oliva, Rogerio Feris<br>View a PDF of the paper titled ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding, by Jovana Kondic and Pengyuan Li and Dhiraj Joshi and Isaac Sanchez and Ben Wiesel and Shafiq Abedin and Amit Alfassy and Eli Schwartz and Daniel Caraballo and Yagmur Gizem Cinar and Florian Scheidegger and Steven I. Ross and Daniel Karl I. Weidele and Hang Hua and Ekaterina Arutyunova and Roei Herzig and Zexue He and Zihan Wang and Xinyue Yu and Yunfei Zhao and Sicong Jiang and Minghao Liu and Qunshu Lin and Peter Staar and Luis Lastras and Aude Oliva and Rogerio Feris

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Abstract:Understanding charts requires models to jointly reason over geometric visual patterns, structured numerical data, and natural language -- a capability where current vision-language models (VLMs) remain limited. We introduce ChartNet, a high-quality, million-scale multimodal dataset designed to advance chart interpretation and reasoning. ChartNet leverages a novel code-guided synthesis pipeline to generate 1.5 million diverse chart samples spanning 24 chart types and 6 plotting libraries. Each sample consists of five aligned components: plotting code, rendered chart image, data table, natural language summary, and question-answering with reasoning, providing fine-grained cross-modal alignment. To capture the full spectrum of chart comprehension, ChartNet additionally includes specialized subsets encompassing human annotated data, real-world data, safety, and grounding. Moreover, a rigorous quality-filtering pipeline ensures visual fidelity, semantic accuracy, and diversity across chart representations. Fine-tuning on ChartNet consistently improves results across benchmarks, demonstrating its utility as large-scale supervision for multimodal models. As the largest open-source dataset of its kind, ChartNet aims to support the development of foundation models with robust and generalizable capabilities for data visualization understanding. The dataset is publicly available at this https URL

Comments:<br>Accepted at CVPR 2026

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

Cite as:<br>arXiv:2603.27064 [cs.CV]

(or<br>arXiv:2603.27064v2 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2603.27064

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arXiv-issued DOI via DataCite

Submission history<br>From: Jovana Kondic [view email]<br>[v1]<br>Sat, 28 Mar 2026 00:45:05 UTC (8,436 KB)

[v2]<br>Tue, 14 Apr 2026 20:08:27 UTC (8,436 KB)

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View a PDF of the paper titled ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding, by Jovana Kondic and Pengyuan Li and Dhiraj Joshi and Isaac Sanchez and Ben Wiesel and Shafiq Abedin and Amit Alfassy and Eli Schwartz and Daniel Caraballo and Yagmur Gizem Cinar and Florian Scheidegger and Steven I. Ross and Daniel Karl I. Weidele and Hang Hua and Ekaterina Arutyunova and Roei Herzig and Zexue He and Zihan Wang and Xinyue Yu and Yunfei Zhao and Sicong Jiang and Minghao Liu and Qunshu Lin and Peter Staar and Luis Lastras and Aude Oliva and Rogerio Feris<br>View PDF<br>HTML (experimental)<br>TeX Source

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