An End-to-end Pytorch-Lightning implemented CAPTCHA OCR model. Training 2 epoch under 100k images to get over 96% acc on Val dataset 🤩. with 200k or even more training set you may get >98% acc

The basic usage of {captcha} involves the functions read_captcha(), plot(), captcha_annotate(), captcha_load_model() and decrypt(). The diagram below summarizes the relationships between these functions.

Jul 13, 2020 · In this approach, we train the model for each character-position separately i.e. create a separate model for each position. Then, we use these models one-by-one on an image to solve for each...

May 12, 2025 · An AI-powered CAPTCHA solver built with PyTorch that achieves up to 100% accuracy on the Fournier CAPTCHA Version 2 dataset. This repository provides a high-performance training pipeline, mixed-precision GPU acceleration, and an easy-to-use inference script.

Sep 25, 2020 · Invert the CAPTCHA, segment the characters, and save each alphanumeric character separately to disk (with its label). Train a neural network to recognize the characters of each class. To use our model, feed-in any CAPTCHA, invert the image, and segment the characters.

Mar 18, 2020 · Step by step code to solve Captcha with Deep Learning and Machine Learning using Python, PyTorch, CV Open and Pillow. Figures and code snippets are also included.

One can also serialize the model to use it with other packages such as PyTorch. A tutorial on serialization can be found in the torch package documentation. Fitting a new Captcha model can be challenging. To help with that, the captcha package documentation provides an application example.

1 day ago · Each CAPTCHA type requires a special solving approach. A universal CAPTCHA solver remains a challenge: the bot system must read text, classify pictures, or synthesize behavior, depending on the check encountered. ... The code uses PyTorch and TensorFlow, and for image processing — OpenCV and Pillow. Using the pytesseract library as a control ...

This project uses a combination of a CNN and a bidirectional LSTM to solve CAPTCHA tests by detecting and identifying sequences of characters. The CNN is used to gather local information about the image and the LSTM is used to learn the sequencing of the characters.

Captcha recognition using PyTorch Description. Trained weight is exist in weights folder. Implementation of RNN for Captcha recognition using PyTorch.