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line-graphics-dataset

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metadata
license: cc-by-4.0
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
dataset_info:
  features:
    - name: image
      dtype: image
    - name: image_name
      dtype: string
    - name: width
      dtype: int64
    - name: height
      dtype: int64
    - name: instances
      list:
        - name: category_id
          dtype: int64
        - name: mask
          sequence:
            sequence: float64
  splits:
    - name: train
      num_bytes: 8927542
      num_examples: 200
    - name: validation
      num_bytes: 4722935
      num_examples: 100
    - name: test
      num_bytes: 3984722
      num_examples: 100
  download_size: 16709320
  dataset_size: 17635199

Line Graphics (LG) dataset

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

By Omar Moured et al.

Dataset Summary

The dataset features instance segmentation masks for 400 real line chart images manually labeled 11 categories by professionals. Images are collected from 5 different prfessions to increase the richfullness. In our paper we studedi two level of segmentations, coarse-level where we segment (spines, axis-labels, legend, lines, titles), and fine-level where we have for each category an x and y subclasses (except legend and lines) and also each line is segmented seperatly.

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)

  • images - 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: