PHPWind Captcha OCR
An ONNX OCR model trained on four-digit numeric captcha images from one legacy PHPWind deployment. It runs entirely on the local machine: no external API or GPU is required.
PHPWind reference implementation: alibaba/phpwind
contains the PwVerifyCode and PwGDCode classes targeted by this model.
中文文档: README_zh.md
Answer for the captcha shown above:
9125
Scope and responsible use
This model is intended for PHPWind site operators, developers, and researchers working with PHPWind deployments they own or are explicitly authorized to test. Use it for local integration tests, accessibility research, or evaluation of your own captcha implementation. Do not use it to automate account logins or bypass access controls.
Different PHPWind versions and custom themes can generate visually different captchas. Validate on representative, authorized samples before deployment.
Version support
This checkpoint was trained only on four-digit captcha images from a target
deployment whose footer displayed v0.7β. This is an observed deployment
label, not a claim about an official PHPWind release version.
| Deployment or version label | Status | Evidence | Notes |
|---|---|---|---|
Target deployment — footer label v0.7β | Training scope | 997 manually labelled images; 88.61% held-out validation accuracy | The only visual configuration represented in the training and reference evaluation data. |
| Other PHPWind releases, forks, themes, or captcha generators | Unverified | No version-specific evaluation | Validate with authorized representative samples; fine-tune if the visual distribution differs. |
Quick start
Install the runtime:
pip install onnxruntime pillow numpy
Run local inference on a captcha image you are authorized to process:
import numpy as np
import onnxruntime as ort
from PIL import Image
session = ort.InferenceSession("model.onnx", providers=["CPUExecutionProvider"])
def predict_captcha(path: str) -> str:
image = Image.open(path).convert("RGB").resize((160, 64), Image.BILINEAR)
inputs = np.asarray(image, dtype=np.float32).transpose(2, 0, 1)[None] / 255.0
logits = session.run(None, {"input": inputs})[0]
return "".join(str(int(logits[0, position].argmax())) for position in range(4))
print(predict_captcha("captcha.png"))
Model interface
| Item | Value |
|---|---|
| Input | input: [batch, 3, 64, 160], float32, RGB values in [0, 1] |
| Output | logits: [batch, 4, 10]; argmax per position gives one digit |
| Preprocessing | RGB → resize to 160 × 64 (bilinear) → divide by 255 |
| Format | ONNX, opset 18 |
| Runtime | CPU supported; no GPU requirement |
Evaluation
The published checkpoint reached 88.61% validation accuracy on a held-out
split of 997 manually labelled images from the target v0.7β footer-label
deployment. This is a model-card reference metric, not a guarantee for another
PHPWind version, theme, or deployment. See the evaluation
protocol for the scope and reproducibility requirements.
Documentation
- Inference guide — Python and Go integration details
- Training and fine-tuning — data preparation and model training
- Evaluation — offline validation protocol
- Documentation index
Training data and license
The checkpoint was trained from scratch with a position-preserving CNN on 997 manually labelled images. For adaptation, use only captcha images from PHPWind deployments you operate or are authorized to evaluate.
This project is licensed under GNU AGPL-3.0. Modified or networked derivative works must meet the license's corresponding-source requirements.