Update README.md

README.md

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----
-license: cc-by-nc-4.0
-dataset_info:
-  config_name: real
-  features:
-  - name: id
-    dtype: int64
-  - name: page_img
-    dtype: string
-  - name: bbox
-    list: int64
-  - name: centroid
-    list: float64
-  - name: box_area
-    dtype: int64
-  - name: label
-    dtype: int64
-  - name: comp_size
-    list: int64
-  - name: file
-    dtype: string
-  - name: image
-    dtype: image
-  splits:
-  - name: train
-    num_bytes: 598442370
-    num_examples: 97223
-  - name: test
-    num_bytes: 30473923
-    num_examples: 1000
-  download_size: 643532810
-  dataset_size: 628916293
-configs:
-- config_name: real
-  data_files:
-  - split: train
-    path: real/train-*
-  - split: test
-    path: real/test-*
----
-Dataset comming soon!
+# Math-Strike Dataset
+
+## Dataset Description
+
+**Math-Strike** is a novel dataset featuring real and synthetic strike-outs, component-level annotations, and aligned LaTeX, designed to support research on strike-out removal and handwritten mathematical formula recognition. 
+
+Handwritten STEM manuscripts often contain strike-outs, overwrites, and other markings that disrupt mathematical content structure and reduce OCR/VLM recognition accuracy. This dataset provides a rigorous benchmark for training and evaluating models on connected-component classification, geometry-aware inpainting, and downstream compilable LaTeX generation.
+
+The collection originates from the 2024-2025 mid-term and final examinations of a university's Multivariate Calculus Elite class. 
+
+### Dataset Subsets
+
+The dataset is divided into two main subsets, easily accessible via Hugging Face's configuration system:
+
+- **`real` (Math-Strike-Real)**: Contains only real, authentic strike-outs collected directly from student exam papers.
+- **`synth` (Math-Strike-Synth)**: Contains synthetic strike-outs generated using an augmentation taxonomy of 20 diverse strike-out styles (e.g., wavy, zigzag, multi-crossed, blackout) to address class imbalance and improve model robustness against complex occlusion patterns.
+
+Additionally, the `original_img` folder is provided in the repository, containing the full page-level raw scans with minimal border cropping and all original marks.
+
+## Dataset Structure
+
+### Data Instances
+
+When loading the dataset via the `datasets` library, each instance represents a single connected component extracted from the manuscript.
+
+**Example instance:**
+
+```json
+{
+    "id": 85235,
+    "page_img": "20250523024843071_page001.png",
+    "bbox": [5694, 6677, 91, 112],
+    "centroid": [5739.5, 6733.0],
+    "box_area": 4506,
+    "label": 0,
+    "comp_size": [112, 91],
+    "image": "<...>"
+    "file": "<...>"
+}
+```
+
+### Data Fields
+
+- **`id`** (int): Global component identifier.
+- **`page_img`** (string): Source page filename.
+- **`bbox`** (list): Bounding box coordinates in pixels.
+- **`centroid`** (list): Geometric centre (x, y) of the component.
+- **`box_area` / `pixel_area`** (int): Foreground-pixel count within the component.
+- **`label`** (int): 0 = retained (valid mathematical symbol), 1 = removed (strike-out/noise).
+- **`comp_size`** (list): Height and width of the bounding box.
+- **`image`** (Image): The actual cropped PNG image of the component.
+- **`file`** (string): The actual path of the image.
+
+## Usage
+
+You can easily load the dataset using the Hugging Face `datasets` library. The images and metadata are already pre-aligned in Parquet format.
+
+```python
+from datasets import load_dataset
+
+# Load the subset with real strike-outs
+dataset_real = load_dataset("Incinciblecolonel/MathStrike", name="real")
+
+# Load the subset with synthetic strike-outs
+dataset_synth = load_dataset("Incinciblecolonel/MathStrike", name="synth")
+
+# Access the training split
+train_data = dataset_real["train"]
+print(train_data[0]) 
+
+# Directly visualize the component image
+train_data[0]["image"].show()
+```
+
+## License
+
+The Math-Strike dataset is distributed under the **CC-BY-NC 4.0** license for non-commercial research and educational use.
+
+## Citation
+
+If you find this dataset useful for your research, please cite our paper:
+
+```
+@article{shen2026mathscrub,
+  title={MathScrub: Single-Pipeline Strike-Out Removal and LaTeX Generation for Handwritten Calculus Expressions},
+  author={Shen, Sen and Liu, Ning and Song, Lintao and Han, Dongkun},
+  journal={IEEE Transactions on Learning Technologies},
+  year={2026},
+  publisher={IEEE}
+}
+```