txtcaptcha

Read, annotate, train and decrypt text captchas in images with a modern CRNN + CTC pipeline in PyTorch.

txtcaptcha ships:

• a CRNN architecture that handles arbitrary input sizes and variable-length labels,
• the full alphanumeric vocabulary 0-9a-zA-Z (62 classes + CTC blank),
• decode-time masking so a single trained model can be restricted per site (e.g. mask="[0-9]"),
• fixed-length decoding via length=N for sites with a known length,
• a pretrained unified model hosted on the Hugging Face Hub with ~89% captcha-level accuracy across ten Brazilian court captcha datasets.

Installation

pip install txtcaptcha

Or from source with uv:

git clone https://github.com/jtrecenti/txtcaptcha
cd txtcaptcha
uv sync --extra dev

Quick start

The first decrypt call downloads the pretrained model from the Hugging Face Hub into ~/.cache/huggingface/hub; subsequent calls are free.

from txtcaptcha import read_captcha, decrypt

cap = read_captcha("path/to/captcha.png")
print(decrypt(cap))                          # greedy, variable length
print(decrypt(cap, mask="[0-9]"))            # digits only
print(decrypt(cap, length=5))                # force exactly 5 chars
print(decrypt(cap, mask=list("abcdef0123"))) # explicit allowed set

Pin a specific release or load a different Hub repo explicitly:

from txtcaptcha import from_pretrained

model = from_pretrained("jtrecenti/txtcaptcha-crnn", revision="v0.1.0")
print(decrypt(cap, model=model))

Training your own model

from txtcaptcha import fit_model, save_model, download_dataset

data_dir = download_dataset("tjmg", "data")
model, history = fit_model(
    data_dir,
    epochs=30,
    batch_size=64,
    case_sensitive=False,
)
save_model(model, "tjmg.pt")

Publishing your own model to the Hub

from txtcaptcha import push_to_hub

push_to_hub(
    model,
    repo_id="your-username/your-captcha-model",
    model_card="# My captcha model\n\nTrained on ...",
    tag="v0.1.0",
)

Public API

Full API reference: https://jtrecenti.github.io/txtcaptcha/.

Architecture

CRNN is a Convolutional Recurrent Neural Network:

1. CNN backbone — ResNet-style basic blocks (64 → 128 → 256 → 256 channels) with strided pooling. Down-samples height by 8 and width by 4, preserving width resolution for the sequence dimension.
2. Adaptive pool — collapses the remaining height to 1, producing a width-indexed sequence of feature vectors.
3. BiLSTM — 2-layer bidirectional LSTM (hidden 256).
4. Linear head — projects to len(vocab) + 1 logits per timestep (the extra slot is the CTC blank).
5. CTC loss — handles variable-length targets, no per-position softmax.

Variable image dimensions are handled by resizing height to 32 at load time, preserving the aspect-ratio width, and padding widths within each batch via pad_collate. CRNN+CTC is the de-facto baseline for short-text scene-text recognition — lighter than transformer OCR (e.g. TrOCR) and consistently strong on short captcha images.

Variable-length labels

CRNN + CTC handles variable label lengths natively. The convolutional stack emits T logits per image; CTC collapsing (remove consecutive repeats, then remove blanks) turns any path into a string of arbitrary length between 0 and T. Training mixes 4-char and 5-char labels in the same batch — no length head, no padding tokens.

The downside of greedy CTC is that a confident wrong timestep can yield a prediction of the wrong length. When you know the expected length, pass length= to switch to an exact dynamic-programming search over CTC paths that collapse to exactly that many characters:

decrypt(cap)                       # greedy
decrypt(cap, length=5)             # force 5 chars
decrypt(cap, length=4, mask="[0-9]")  # combine with masking

The DP runs in O(T · L · |vocab|) per image, tracks the best path for every (collapsed_count, last_index) state and reconstructs the argmax. It is strictly at least as good as greedy when the true length is known and never emits a wrong-length prediction.

Decode-time masking

decrypt(..., mask=...) zeros out forbidden vocabulary logits before CTC decoding, so the same trained model can be specialized per site:

decrypt(cap, mask=["a", "b", "c", "1", "2", "3"])  # explicit list
decrypt(cap, mask="[0-9a-z]")                       # regex char-class
decrypt(cap, mask="[A-Z]", case_sensitive=True)     # uppercase only
decrypt(cap, mask="[a-z]", case_sensitive=False)    # output lowercased

Notebooks

• notebooks/train_unified_model.ipynb — downloads every dataset, merges them and trains the unified CRNN. Designed for a cloud GPU machine.
• notebooks/eval_per_dataset.ipynb — per-dataset accuracy on a held-out split.
• notebooks/eval_per_dataset_live.ipynb — predictions on freshly downloaded, unlabeled captchas (overfit check).

Tests

uv run pytest

License

MIT © Julio Trecenti