What it does

Trains a convolutional neural network to recognize 4-character CAPTCHAs end-to-end. You generate synthetic training data with ImageCaptcha, run the training script, and get a model.pkl that predicts digits, uppercase, or lowercase letters from raw images. No manual cropping, no resizing rituals, no hand-labeling individual characters.

The interesting bit

The project treats CAPTCHA breaking as multi-label classification with one-hot encoded positions—each of the four slots gets its own softmax head. The synthetic-data loop is the practical core: since ImageCaptcha generates the labels automatically, you can mint 10,000+ training examples in minutes rather than paying for mechanical turk or dark-web CAPTCHA-solving APIs.

Key highlights

• Claims 99.9999% accuracy on pure 4-digit CAPTCHAs, ~96% on digits plus uppercase letters (per README; no independent verification shown)
• End-to-end pipeline: captcha_gen.py → captcha_train.py → captcha_test.py → captcha_predict.py
• Uses standard PyTorch + ImageCaptcha stack; Python 2.7+ supported
• One-hot encoding per character position, trained for ~15 epochs to reach reported 96%+ accuracy
• Explicitly framed as educational/demonstration use only

Caveats

• Only tested against synthetically generated CAPTCHAs from ImageCaptcha; real-world CAPTCHA systems with distortion, noise lines, or variable fonts will likely break it
• Python 2.7 baseline is dated; modern environments will need compatibility tweaks
• No code-level documentation of the CNN architecture depth, regularization, or training hyperparameters beyond “multi-layer” and “about 15 epochs”

Verdict

Worth a look if you’re teaching or learning how CNNs handle structured multi-character recognition, or if you need a quick baseline for a very specific, weak CAPTCHA variant. Skip it if you need production-grade CAPTCHA solving or want battle-tested code against modern anti-bot systems.