File size: 3,506 Bytes
e6c4023 da18d64 e6c4023 76d7feb aa4f8b6 76d7feb aa4f8b6 76d7feb aa4f8b6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
---
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: 8927542.0
num_examples: 200
- name: validation
num_bytes: 4722935.0
num_examples: 100
- name: test
num_bytes: 3984722.0
num_examples: 100
download_size: 16709320
dataset_size: 17635199.0
---
# Line Graphics (LG) dataset
This is the official page for the LG dataset for our paper [Line Graphics Digitization: A Step Towards Full Automation](https://link.springer.com/chapter/10.1007/978-3-031-41734-4_27).
By [Omar Moured](https://www.linkedin.com/in/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
```python
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
```python
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:
```python
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
```bibtex
@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
|