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Captcha Break

Captcha Break is an open-source Jupyter Notebook project that demonstrates generating labeled image CAPTCHAs and training a Keras deep convolutional neural network to recognize their text. Its example uses four-character strings made from digits and uppercase letters, with generated data supplied through a Keras Sequence-based generator. The notebook also covers model visualization and training callbacks, and its cod

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Independent OpenViST project profile, based on linked sources.

Captcha Break — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it to learn or adapt a notebook-based training workflow when your task involves image CAPTCHA recognition and you can work with generated examples and the listed Python machine-learning stack.

Who it is for

  • Developers and learners exploring deep-learning OCR for image CAPTCHA text.
  • Notebook users who want an example of generating labeled training data and training a Keras model.

Documented uses

  • Generate synthetic image CAPTCHA samples with known character labels.
  • Prepare batches of images and character labels for model training.
  • Build and train a convolutional neural network for the example CAPTCHA format.
  • Visualize the model and record training loss.
Before you choose

Requirements & limits

The repository is public and identified as MIT-licensed. The README lists a GPU-oriented TensorFlow environment and recommends a GPU for training.

  • The included data generator creates synthetic CAPTCHAs; performance on unrelated or real-world CAPTCHA formats is not documented.
  • The README lists older, specific dependency versions, so users should account for their environment when adapting the project.
  • GPU use is recommended, and the README cautions that training may be lengthy on a laptop.
  • Visualization requires additional listed packages, including pydot and Graphviz.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Train the example model on generated CAPTCHA images

Follow the documented notebook workflow to generate synthetic CAPTCHA data and train a character-recognition model.

Before you start

  • Jupyter Notebook and Python packages listed by the project: captcha 0.3, tensorflow-gpu 1.13.1, NumPy 1.16.4 and tqdm 4.28.1.
  • A GPU is recommended by the project; matplotlib, pandas, pydot and Graphviz are listed for visualization.

Steps

  1. Set the CAPTCHA character set and image dimensions in the notebook; the example uses digits and uppercase letters.

  2. Create a data generator that returns batches of generated images and character labels.

  3. Define the convolutional Keras model and compile it with the documented training configuration.

  4. Train with generated training and validation batches, using callbacks to stop on stalled loss, save the best model weights and log loss.

Expected outcome

A trained model checkpoint and CSV training log from the notebook’s synthetic CAPTCHA recognition workflow.

Guide limitations

  • The example targets generated four-character CAPTCHAs using digits and uppercase letters; results for other CAPTCHA formats are not established.
  • The README recommends a GPU and warns training may take a long time on a laptop.
  • The listed software versions are the README’s environment, not a guarantee of compatibility with current releases.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
16 October 2026
Original website title
ypwhs/captcha_break · Official homepage title · retained in its source language
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