Update README.md

README.md

@@ -40,18 +40,88 @@ dataset_info:
   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).
 
-categories:
- 0: "Label"
- 1: "Legend"
- 2: "Line"
- 3: "Spine"
- 4: "Title"
- 5: "ptitle"
- 6: "xlabel"
- 7: "xspine"
- 8: "xtitle"
- 9: "ylabel"
- 10:"yspine"
- 11:"ytitle"
+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