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--- |
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license: cc-by-4.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: ocr_text |
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dtype: string |
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- name: result |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 60582512.0 |
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num_examples: 10000 |
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- name: test |
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num_bytes: 70989855.334 |
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num_examples: 11766 |
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download_size: 132297385 |
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dataset_size: 131572367.334 |
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task_categories: |
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- question-answering |
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tags: |
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- captcha |
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- math |
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- mathcaptcha |
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- math-captcha |
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- mvccaptcha |
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--- |
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<p align="center"> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/65e3c559d26b426e3e1994f8/gI5XYkSxvcw3E9GAfDEZG.png" /> |
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</p> |
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<div align="center"> |
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</div> |
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## Dataset Details |
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* **Dataset Name:** MathCaptcha10k |
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* **Curated by:** Atalay Denknalbant |
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* **License:** Creative Commons Attribution 4.0 International (CC BY 4.0) |
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* **Repository:** [https://www.kaggle.com/datasets/atalaydenknalbant/mathcaptcha10k](https://www.kaggle.com/datasets/atalaydenknalbant/mathcaptcha10k) |
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### Dataset Description |
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A corpus of 10 000 synthetic arithmetic‐captcha images rendered at 200×70 px. Each image contains exactly two base-10 numbers (1–2 digits), a single `+` or `–` operator, an `=` sign and a trailing question mark (e.g. `96-41=?`). Every example in the **train** split includes: |
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| image | ocr\_text | result | |
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| -------------------------- | --------- | ------ | |
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| `96-41=?` | "96-41=?" | 55 | |
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…where `ocr_text` is the exact characters in the image, and `result` is the integer answer. |
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The **test** split consists of 11 766 unlabeled captchas in `Unlabeled/` folder. |
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--- |
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## Examples of the Captchas |
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**Easy example** |
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**Challenging example** |
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> Even state-of-the-art vision-language models often mis‐OCR the more distorted variants (see the “challenging” sample above). |
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--- |
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## Uses |
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* **Direct uses**: |
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* Train and evaluate OCR/vision-language models on simple arithmetic recognition. |
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* Benchmark visual math-solving capabilities. |
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* **Out-of-scope uses**: |
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* Handwritten digit OCR. |
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* Complex mathematical notation beyond two-term arithmetic. |
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--- |
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## Dataset Structure |
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* **Splits** |
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* `train` (10 000 labeled examples) |
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* `test` (11 766 `.png` files in `Unlabeled/`) |
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* **Features** |
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* `image` (PNG file) |
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* `ocr_text` (string, e.g. `"75-26=?"`) |
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* `result` (int, e.g. `49`) |
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--- |
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## Dataset Creation |
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### Curation Rationale |
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Synthetic captchas provide a controlled environment for training and benchmarking. Even top tier vision language methods struggle with some distortions motivating manual QA to ensure label accuracy. |
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### Source Data |
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Programmatically generated using [CaptchaMvc.Mvc5](https://www.nuget.org/packages/CaptchaMvc.Mvc5)’s standard arithmetic template. |
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### Data Collection & Processing |
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1. Generate 10 000 PNG captchas via CaptchaMvc.Mvc5. |
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2. Run a VLM-based OCR pipeline, then manually verify and correct every label in a Streamlit QA app. |
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**Annotator:** |
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* Atalay Denknalbant |
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--- |
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## Personal & Sensitive Information |
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None. Captchas contain no personal data. |
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--- |
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## Bias, Risks & Limitations |
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* Purely synthetic; may not generalize to natural or handwritten text. |
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* Limited to two-term, 1–2 digit arithmetic. |
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--- |
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## Recommendations |
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Combine with broader OCR datasets for real-world text recognition tasks. |
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--- |
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## Citation |
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```bibtex |
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@misc{atalay_denknalbant_2025, |
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title = {MathCaptcha10k}, |
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author = {Atalay Denknalbant}, |
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year = {2025}, |
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howpublished = {\url{https://www.kaggle.com/ds/7779792}}, |
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publisher = {Kaggle}, |
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DOI = {10.34740/KAGGLE/DS/7779792} |
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} |
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``` |
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**APA** |
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> Denknalbant, A. (2025). *MathCaptcha10k* \[Data set]. Kaggle. [https://doi.org/10.34740/KAGGLE/DS/7779792](https://doi.org/10.34740/KAGGLE/DS/7779792) |
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## Dataset Card Authors |
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* Atalay Denknalbant |
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## Dataset Card Contact |
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* Atalay Denknalbant (questions & feedback) |
