omoured/line-graphics-dataset

Line Graphics (LG) dataset

This is the official page for the LG dataset, as featured in our paper Line Graphics Digitization: A Step Towards Full Automation.

By Omar Moured et al.

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.

Category ID Reference

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

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 labeled instances, each instance dictionary has {category_id, annotations}. The annotations are in COCO format.

Sample Usage

from datasets import load_dataset

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

# Access the training split
train_dataset = dataset["train"]

# Print sample data
print(dataset["train"][0])

You can render the masks using pycocotools library as follows:

from pycocotools import mask

polygon_coords = dataset['train'][0]['instances'][1]['mask']
image_width = dataset['validation'][0]['width']
image_height = dataset['validation'][0]['height']

mask_binary = mask.frPyObjects(polygon_coords, image_height, image_width) 

segmentation_mask = mask.decode(mask_binary)

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, omar.moured@kit.edu