https://github.com/LightQuantumArchive/jaccount-captcha-solver
High accuracy captcha solver for SJTU Jaccount login page using SVM and ResNet.
https://github.com/LightQuantumArchive/jaccount-captcha-solver
Last synced: about 1 year ago
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High accuracy captcha solver for SJTU Jaccount login page using SVM and ResNet.
- Host: GitHub
- URL: https://github.com/LightQuantumArchive/jaccount-captcha-solver
- Owner: LightQuantumArchive
- License: apache-2.0
- Created: 2020-01-25T15:53:54.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-11-08T20:05:52.000Z (over 3 years ago)
- Last Synced: 2025-03-17T19:22:01.836Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 3.14 MB
- Stars: 44
- Watchers: 2
- Forks: 5
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Jaccount Captcha Solver
High accuracy captcha solver for SJTU Jaccount login page using SVM and ResNet.
This work includes two high performance recognizers.
The SVM based recognizer has an accuracy of 90%. It first applies projection-based algorithm to the input image, then use a pre-trained SVM model
to predict the answer. It's memory and cpu efficient.
The Resnet based recognizer achieves an average accuracy of 99%. It feeds the image directly into a pre-trained ResNet-20 model to predict the answer.
It consumes more memory and computing power.
## Getting Started
These instructions will get you a copy of the project up for development and testing purposes.
### Prerequisites
- Python >= 3.7
- OpenCV >= 2.0
- Tesseract
Optionally,
- A CUDA device
### Installing
First, clone this repository.
```shell script
$ git clone https://github.com/PhotonQuantum/jaccount-captcha-solver
```
It's strongly recommended to setup a virtual environment first to avoid polluting global packages.
```shell script
$ python -m venv .venv
$ source ./.venv/bin/activate
```
Then you may install required packages.
```shell script
$ pip install -r requirements.txt
```
Now you are ready to go!
> For Archlinux users:
>
> Currently it's recommended to install pyenv to manage your python version and freeze your python version to 3.7.
>
> Several packages this project depends on doesn't provide py38 wheels for now.
*For instructions on fetching a dataset, training your model, and benchmarking it, please refer to [USAGE.md](USAGE.md) for more information.*
## Deployment
### pysjtu
You need to compile your model into ONNX format if you haven't done so yet. For instructions, see [USAGE.md](USAGE.md).
NNRecognizer feeds the whole captcha image into your model.
Any model with an input of `[1, 1, *, *]` and an output of `[tensor[26], tensor[26], tensor[26], tensor[26], tensor[27]]` is accepted by `NNRecognizer`.
LegacyRecognizer feeds segmented images (each contains only one character) into your model. Any model with an input of `[*, *]` and an output of `[str, ...]` is accepted by `LegacyRecognizer`.
```python
from pysjtu import LegacyRecognizer, NNRecognizer, Session
svm_ocr = LegacyRecognizer(model_file="svm_model.onnx")
nn_ocr = NNRecognizer(model_file="nn_model.onnx")
session = Session(ocr=svm_ocr) # or Session(ocr=nn_ocr)
```
### Other
`ocr_legacy.py` provides `SVMRecognizer` and `NNRecognizer`. Either of them accepts raw models. It depends on `nn_models.py` and `utils.py`.
`ocr.py` provides `LegacyRecognizer` and `NNRecognizer`. Either of them accepts ONNX models. It depends on `utils.py`.
You may integrate them into your project:
```python
>>> import PIL from Image
>>> from ocr_legacy import NNRecognizer
>>> recognizer = NNRecognizer(model_file="ckpt.pth")
>>> img = Image.open("captcha.jpg")
>>> recognizer.recognize(img)
gbmke
```
## Built With
* [PyTorch](https://pytorch.org/) - An open source machine learning framework that accelerates the path from research prototyping to production deployment.
* [pytorch_resnet_cifar10](https://github.com/akamaster/pytorch_resnet_cifar10) - a proper ResNet implementation for CIFAR10/CIFAR100 in pytorch.
* [SciKit-Learn](https://scikit-learn.org/) - A free software machine learning library for the Python programming language.
* [Tesseract](https://github.com/tesseract-ocr/tesseract/) - an OCR engine with support for unicode and the ability to recognize more than 100 languages out of the box.
* [Pillow](https://python-pillow.org/) - The friendly PIL fork.
* [Matplotlib](https://matplotlib.org/) - A Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.
* [HTTPX](https://www.python-httpx.org/) - A next generation HTTP client for Python.
* [OpenCV](https://opencv.org/) - An open source computer vision and machine learning software library.
## License
This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details
## Acknowledgments
- [T.T. Tang](https://github.com/ElectronicElephant) for his idea and support on training a ResNet-20 model
to do end-to-end multi-task learning on captcha images.
- [Yerlan Idelbayev](https://github.com/akamaster) for his ResNet implementation.