FlanChanXwO/phpwind-captcha-ocr

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.