omoured/line-graphics-dataset

Line Graphics Digitization: A Step Towards Full Automation

Omar Moured, Jiaming Zhang, Alina Roitberg, Thorsten Schwarz, Rainer Stiefelhagen

Dataset Summary

The dataset includes instance segmentation masks for 400 real line chart images, manually labeled into 11 categories by professionals. These images were collected from 5 different professions to enhance diversity. In our paper, we studied two levels of segmentation: coarse-level, where we segmented (spines, axis-labels, legend, lines, titles), and fine-level, where we further segmented each category into x and y subclasses (except for legend and lines), and individually segmented each line.

Dataset structure (train, validation, test)

• image - contains the PIL image of the chart
• image_name - image name with PNG extension
• width - original image width
• height - original image height
• instances - contains N number of COCO format instances. Check the sample visulization code below.

Sample Usage

[optional] install pycocotools to rendeder masks with below code.

from datasets import load_dataset
from pycocotools import mask
import matplotlib.pyplot as plt
import random

# Load dataset
ds = load_dataset("omoured/line-graphics-dataset")

# Class ID to name
id_to_name = {
    0: "Label", 1: "Legend", 2: "Line", 3: "Spine",
    4: "Title", 5: "ptitle", 6: "xlabel", 7: "xspine",
    8: "xtitle", 9: "ylabel", 10: "yspine", 11: "ytitle"
}

# Random image + instance
sample = random.choice(ds["val"])
img = sample["image"]
i = random.randint(0, len(sample["instances"]["mask"]) - 1)

# Get mask + class info
poly = sample["instances"]["mask"][i]
cat_id = sample["instances"]["category_id"][i]
cat_name = id_to_name.get(cat_id, "Unknown")

# Decode and plot
rle = mask.frPyObjects(poly, sample["height"], sample["width"])
bin_mask = mask.decode(rle)

plt.imshow(img)
plt.imshow(bin_mask, alpha=0.5, cmap="jet")
plt.title(f"imgname: {sample['image_name']}, inst: {cat_name}")
plt.axis("off")
plt.show()

Copyrights

This dataset is published under the CC-BY 4.0 license, which allows for unrestricted usage, but it should be cited when used.

Citation

@inproceedings{moured2023line,
  title={Line Graphics Digitization: A Step Towards Full Automation},
  author={Moured, Omar and Zhang, Jiaming and Roitberg, Alina and Schwarz, Thorsten and Stiefelhagen, Rainer},
  booktitle={International Conference on Document Analysis and Recognition},
  pages={438--453},
  year={2023},
  organization={Springer}
}

Contact

If you have any questions or need further assistance with this dataset, please feel free to contact us:

• Omar Moured, [email protected]