README.md

---
license: apache-2.0
task_categories:
- image-to-text
language:
- en
pretty_name: dataset-BotDetect-CAPTCHA-Generator
size_categories:
- 100M<n<1B
---
📦 CAPTCHA Datasets (style0–style59)
====================================

This repository contains CAPTCHA datasets for training CRNN+CTC models. Each archive `dataset_*.tar.gz` includes **60 styles** (from [BotDetect Captcha](https://captcha.com/)), structured as folders `style0` through `style59`. Each style contains _N_ images depending on the archive name.

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🗂️ Available Archives
----------------------

*   `dataset_500.tar.gz` → 500 images per style (≈ 30,000 total)
*   `dataset_1000.tar.gz` → 1,000 images per style (≈ 60,000 total)
*   `dataset_5000.tar.gz` → 5,000 images per style (≈ 300,000 total)
*   `dataset_10000.tar.gz` → 10,000 images per style (≈ 600,000 total)
*   `dataset_20000.tar.gz` → 20,000 images per style (≈ 1,200,000 total)
*   `dataset_1000_rand.tar.gz` → randomized variant with 1,000 images per style

**Naming convention:** `dataset_{N}.tar.gz` means each `styleX` folder holds exactly `N` PNG images.

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📁 Directory Layout
-------------------

    /path/to/dataset
    ├── style0/
    │   ├── A1B2C.png
    │   ├── 9Z7QK.png
    │   └── ...
    ├── style1/
    │   ├── K9NO2.png
    │   └── ...
    └── ...
        └── style59/
    

*   **Filename** = ground-truth label (5 uppercase alphanumeric chars), e.g. `K9NO2.png`.
*   **Image size** = `50×250` pixels (H=50, W=250), grayscale PNG.
*   **Label rule** = regex `^[A-Z0-9]{5}$` (exactly 5 chars, uppercase & digits).

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🧰 Extraction
-------------

    # example: extract into /workspace/dataset_1000
    mkdir -p /workspace/dataset_1000
    tar -xvzf dataset_1000.tar.gz -C /workspace/dataset_1000
    

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✅ Quick File Counts
-------------------

    # total PNG files (depth 2 to only count inside style folders)
    find /workspace/dataset_1000 -maxdepth 2 -type f -name '*.png' | wc -l
    
    # per-style counts without a for-loop (prints "count styleX")
    find /workspace/dataset_1000 -mindepth 2 -maxdepth 2 -type f -name '*.png' \
    | awk -F/ '{print $(NF-2)}' | sort | uniq -c | sort -k2
    

Expected totals:

*   `dataset_500` → 500 × 60 = 30,000 files
*   `dataset_1000` → 60,000 files
*   `dataset_5000` → 300,000 files
*   `dataset_10000` → 600,000 files
*   `dataset_20000` → 1,200,000 files

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🧪 Label Validation
-------------------

    # list filenames that violate the strict 5-char uppercase/digit rule
    find /workspace/dataset_1000 -type f -name '*.png' \
    | awk -F/ '{print $NF}' | sed 's/\.png$//' \
    | grep -vE '^[A-Z0-9]{5}$' | head
    

CSV report via Python (pandas):

    import os, re
    import pandas as pd
    from glob import glob
    
    root = "/workspace/dataset_1000"
    rows = []
    for s in range(60):
        for p in glob(os.path.join(root, f"style{s}", "*.png")):
            rows.append({"style": f"style{s}", "filepath": p, "label": os.path.basename(p)[:-4]})
    
    df = pd.DataFrame(rows)
    bad = df[~df["label"].str.match(r"^[A-Z0-9]{5}$", na=True)]
    print("Invalid labels:", len(bad))
    if len(bad):
        bad.to_csv("invalid_labels.csv", index=False)
    

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🧩 Example: Load to DataFrame
-----------------------------

    import os
    from glob import glob
    import pandas as pd
    
    def load_dataset(root_dir):
        data = []
        for style_id in range(60):
            folder = os.path.join(root_dir, f"style{style_id}")
            for path in glob(os.path.join(folder, "*.png")):
                label = os.path.splitext(os.path.basename(path))[0]
                data.append((path, label, f"style{style_id}"))
        df = pd.DataFrame(data, columns=["filepath", "label", "style"])
        # enforce strict label rule
        df = df[df["label"].str.match(r"^[A-Z0-9]{5}$")]
        return df
    
    df = load_dataset("/workspace/dataset_1000")
    print(df.head(), len(df))
    

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🔀 Merge Datasets (no loop)
---------------------------

**Add new files without overwriting existing ones**:

    rsync -av \
      --ignore-existing \
      --include='style[0-5][0-9]/' \
      --include='style[0-5][0-9]/*.png' \
      --exclude='*' \
      /workspace/dataset_10000/  /workspace/dataset_20000/
    

**Overwrite only if source is newer**:

    rsync -av --update \
      --include='style[0-5][0-9]/' \
      --include='style[0-5][0-9]/*.png' \
      --exclude='*' \
      /workspace/dataset_10000/  /workspace/dataset_20000/
    

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🔒 Checksums
------------

Optional: keep SHA256 for integrity.

    sha256sum dataset_1000.tar.gz > dataset_1000.tar.gz.sha256
    sha256sum -c dataset_1000.tar.gz.sha256
    

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📝 Notes
--------

*   All images prepared for CRNN+CTC models with input `(H, W) = (50, 250)`, grayscale.
*   Character distribution: digits 0–9 and letters A–Z (uppercase).
*   Each style emulates a distinct visual variant (font/noise/warp) from BotDetect.

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📞 Contact
----------

For questions, dataset issues, or custom subsets, please open an issue in this repository.