| --- |
| license: apache-2.0 |
| task_categories: |
| - image-to-text |
| tags: |
| - captcha |
| - ocr |
| - synthetic |
| - vision-language |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # Synthetic CAPTCHA Dataset |
|
|
| Synthetic distorted-text CAPTCHA images with exact character-string labels, used |
| to fine-tune [`Hansda/minicpm-v4_6-captcha-lora`](https://huggingface.co/Hansda/minicpm-v4_6-captcha-lora). |
|
|
| ## Contents |
|
|
| - **Format:** HuggingFace `imagefolder` (`image`, `text` columns), with |
| `metadata.jsonl` per split. |
| - **Splits:** `train` (10,000) and `test` (2,000) — 12,000 images total. |
| - **Labels:** 4–7 characters from `ABCDEFGHJKLMNPQRSTUVWXYZabcdefghjkmnpqrstuvwxyz23456789` |
| (visually ambiguous `I O 0 1 l` are excluded). |
|
|
| ## Generation |
|
|
| Images are procedurally generated (Pillow + PyTorch) with per-character random |
| font / size / rotation / shear / stretch, wave & bezier warping, background |
| gradients and noise, random lines, curves and arcs, and varied colours — to |
| mimic classic distorted-text CAPTCHAs. |
|
|
| ## Load |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("Hansda/captcha-dataset") |
| print(ds["train"][0]["text"], ds["train"][0]["image"].size) |
| ``` |
|
|
| ## Note |
|
|
| The per-character size is randomized independently, so upper/lower case is not |
| always visually separable for ambiguous glyphs (`c/C`, `s/S`, `v/V`, …). This |
| makes strict case-sensitive scoring inherently hard; case-insensitive reading is |
| the more reliable target. |
|
|
