pyocrcaptcha
基于 YOLO 的图片验证码 OCR Python 包。安装后既可以作为对象调用，也可以直接使用命令行识别图片。
安装
pip install pyocrcaptcha
pip 会根据包元数据自动安装运行依赖：
ultralytics>=8.3.0Pillow>=10.0.0- Ultralytics 的传递依赖，包括 PyTorch、TorchVision、NumPy、OpenCV 等
因此不需要再手动安装 requirements.txt。如果需要确认依赖解析结果，可以执行：
python -m pip show pyocrcaptcha
python -m pip check
发布到 PyPI
仓库通过 .github/workflows/workflow.yml 使用 PyPI Trusted Publishing 发布，不需要保存 API Token。工作流会在发布 GitHub Release 时自动运行，也可以在 GitHub Actions 页面手动运行。
首次发布前，需要在 PyPI 的 Trusted Publisher 设置中填写：
PyPI project name: pyocrcaptcha
Owner: Moxin1044
Repository: pyocrcaptcha
Workflow: workflow.yml
Environment: pypi
工作流会拉取 Git LFS 模型、构建 wheel 和 sdist，并确认 wheel 内包含完整模型后再上传。
从源码安装：
git clone https://github.com/Moxin1044/pyocrcaptcha.git
cd pyocrcaptcha
pip install .
模型也可从 V1.0.0 Release 单独下载：
下载 captcha-character-classifier-yolo11n-100e.pt
命令行
自动判断 4 位或 5 位验证码（支持宽度不是 4/5 整数倍的截图）：
pyocrcaptcha xxx.png
强制指定位数并显示置信度：
pyocrcaptcha xxx.png --positions 4 --details
Python API
from pyocrcaptcha import CaptchaOCR
ocr = CaptchaOCR()
text = ocr("xxx.png")
print(text)
获取置信度和逐字符结果：
result = ocr.recognize("xxx.png")
print(result.text)
print(result.confidence)
print(result.character_confidences)
print(result.positions)
也可以传入自定义模型、设备或固定长度：
ocr = CaptchaOCR(model="best.pt", positions=4, device="cpu")
print(ocr("four_digit.png"))
说明
- 默认模型随包发布，支持数字
0-9和大写字母A-Z。 - 图片字符应大致等宽排列；自动模式会分别尝试 4 位和 5 位并选择平均置信度较高的结果。图片带边距、缩放或宽度不是整数倍也可以识别。
- 当前模型验证集单字符 Top-1 为 83.23%，Top-5 为 94.59%。整串完全正确率会低于单字符准确率。
- 仅供学习与研究使用，请遵守验证码来源网站的服务条款和适用法律法规。
1 maintainer
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