--- license: cc-by-4.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: ocr_text dtype: string - name: result dtype: int64 splits: - name: train num_bytes: 60582512.0 num_examples: 10000 - name: test num_bytes: 70989855.334 num_examples: 11766 download_size: 132297385 dataset_size: 131572367.334 task_categories: - question-answering tags: - captcha - math - mathcaptcha - math-captcha - mvccaptcha ---

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## Dataset Details * **Dataset Name:** MathCaptcha10k * **Curated by:** Atalay Denknalbant * **License:** Creative Commons Attribution 4.0 International (CC BY 4.0) * **Repository:** [https://www.kaggle.com/datasets/atalaydenknalbant/mathcaptcha10k](https://www.kaggle.com/datasets/atalaydenknalbant/mathcaptcha10k) ### Dataset Description 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: | image | ocr\_text | result | | -------------------------- | --------- | ------ | | `96-41=?` | "96-41=?" | 55 | …where `ocr_text` is the exact characters in the image, and `result` is the integer answer. The **test** split consists of 11 766 unlabeled captchas in `Unlabeled/` folder. --- ## Examples of the Captchas **Easy example** ![easy captcha](https://cdn-uploads.huggingface.co/production/uploads/65e3c559d26b426e3e1994f8/HhN9wDAq1zDT6xvY51zHq.png) **Challenging example** ![hard captcha](https://cdn-uploads.huggingface.co/production/uploads/65e3c559d26b426e3e1994f8/zZpwcHMgLt5Y-60wpY5x7.png) > Even state-of-the-art vision-language models often mis‐OCR the more distorted variants (see the “challenging” sample above). --- ## Uses * **Direct uses**: * Train and evaluate OCR/vision-language models on simple arithmetic recognition. * Benchmark visual math-solving capabilities. * **Out-of-scope uses**: * Handwritten digit OCR. * Complex mathematical notation beyond two-term arithmetic. --- ## Dataset Structure * **Splits** * `train` (10 000 labeled examples) * `test` (11 766 `.png` files in `Unlabeled/`) * **Features** * `image` (PNG file) * `ocr_text` (string, e.g. `"75-26=?"`) * `result` (int, e.g. `49`) --- ## Dataset Creation ### Curation Rationale 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. ### Source Data Programmatically generated using [CaptchaMvc.Mvc5](https://www.nuget.org/packages/CaptchaMvc.Mvc5)’s standard arithmetic template. ### Data Collection & Processing 1. Generate 10 000 PNG captchas via CaptchaMvc.Mvc5. 2. Run a VLM-based OCR pipeline, then manually verify and correct every label in a Streamlit QA app. **Annotator:** * Atalay Denknalbant --- ## Personal & Sensitive Information None. Captchas contain no personal data. --- ## Bias, Risks & Limitations * Purely synthetic; may not generalize to natural or handwritten text. * Limited to two-term, 1–2 digit arithmetic. --- ## Recommendations Combine with broader OCR datasets for real-world text recognition tasks. --- ## Citation ```bibtex @misc{atalay_denknalbant_2025, title = {MathCaptcha10k}, author = {Atalay Denknalbant}, year = {2025}, howpublished = {\url{https://www.kaggle.com/ds/7779792}}, publisher = {Kaggle}, DOI = {10.34740/KAGGLE/DS/7779792} } ``` **APA** > Denknalbant, A. (2025). *MathCaptcha10k* \[Data set]. Kaggle. [https://doi.org/10.34740/KAGGLE/DS/7779792](https://doi.org/10.34740/KAGGLE/DS/7779792) ## Dataset Card Authors * Atalay Denknalbant ## Dataset Card Contact * Atalay Denknalbant (questions & feedback)