:scroll: Introduction

Rendering vector graphics into pixel arrays can result in significant memory costs or loss of information, as shown in above Figure 1. We propose the first large-scale chart-based vector graphics dataset focusing on VG-based Detection and Chart Understanding.

Task	Dataset	Type	Source	# Chart Type	# Nums
ChartQA	DVQA	RG	Synthetic	1	300,000
	FigureQA	RG	Synthetic	5	100,000
	PlotQA	RG	Synthetic	3	224,377
	LEAF-QA	RG	Synthetic	4	250,000
Chart-to-Table	ICPR 2020	RG	Synthetic & Real	15	40,322
	ICPR 2022	RG	Real	15	36,183
VG Detection (YOLaT used)	SESYD-Floorplans	VG	Synthetic	-	1,000
	SESYD-Diagrams	VG	Synthetic	-	1,000
VG Detection & Chart-to-Table	VG-DCU(Ours)	VG	Synthetic & Real	16	15,197

The currently available public vector graphics datasets are limited to the two small datasets indicated in the Table and lack the complexity necessary for the advancement of vector image detection. In contrast, our proposed dataset comprises over 10,000 vector charts utilizing diverse primitives with rich attributes.

Dataset Construction

The proposed VG-based chart dataset contains two subsets:

• Vega-Lite: a synthetic subset generated with scripts and fictional data

• Plotly:a real-world subset drawn by users.

Dataset Statistic

Dataset Split: We collect 10,682 synthetic and 4,515 real charts in the VG-DCU dataset, by default using 80% as the training set and 20% as the test set. We divide the training and test set so that objects from the same category are included in both the training and testing set.

	Vega-Lite		Plotly
Chart Type	Train	Test	Train	Test
Area	0	0	87	22
Bar (Vert. & Hor.)	2,400	601	1,704	426
Box (Vert. & Hor.)	0	0	482	121
Donut&Pie	2,395	599	200	50
Line	3,749	938	146	37
Scatter	0	0	640	161
Heatmap	0	0	154	39
Counter	0	0	180	45
Violin	0	0	82	21
Sankey	0	0	21	6
Total	8,544	2,138	3,609	906

Dataset Analysis

(a) The distribution map of categories and number of bbox instances. (b) The width-to-height ratio distribution of class and box instances

Download

We will provide both Baidu Drive and Google Drive for downloading.

• Vega-Lite: Google Drive

• Plotly: Google Drive

Citation

BibTex:

@article{journals/pami/DouJLYSSDWLZ24,
  author       = {Shuguang Dou and
                  Xinyang Jiang and
                  Lu Liu and
                  Lu Ying and
                  Caihua Shan and
                  Yifei Shen and
                  Xuanyi Dong and
                  Yun Wang and
                  Dongsheng Li and
                  Cairong Zhao},
  title        = {Hierarchically Recognizing Vector Graphics and {A} New Chart-Based
                  Vector Graphics Dataset},
  journal      = {{IEEE} Trans. Pattern Anal. Mach. Intell.},
  volume       = {46},
  number       = {12},
  pages        = {7556--7573},
  year         = {2024},
  doi          = {10.1109/TPAMI.2024.3394298},
}

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Related Project

YOLaT-VectorGraphicsRecognition