| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - image-classification |
| - image-to-text |
| viewer: false |
| --- |
| # 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. |
| ### Data Fields |
| | Field | Type | Description | |
| | :--- | :--- | :--- | |
| | `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() |
| ``` |
|
|
| ## Alternative Download (Raw Files) |
| For researchers who prefer downloading the raw image folders and JSON annotations directly instead of using the `datasets` library, the complete archived dataset (ZIP) is hosted on Kaggle: |
|
|
| 👉 **[Download Math-Strike on Kaggle](https://www.kaggle.com/datasets/senshen1020/mathscrub-handwritten-calculus-strike-out-dataset)** |
|
|
|
|
| ## 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} |
| } |
| ``` |
