README.md

metadata

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.

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

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.