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rapid ocr

作者 gaojiren · GitHub ↗ · v1.0.4 · MIT-0
cross-platform ✓ 安全检测通过
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11
当前安装
5
版本数
在 OpenClaw 中安装
/install rapid-ocr
功能描述
专业离线票据OCR工具,支持增值税发票、火车票、飞机票、出租车票等高精度字段提取与批量识别。
安全使用建议
This skill appears to do what it says. Before installing: 1) Be aware that installing the PyPI dependency and the first run will access the network to download ~13MB of model files and will store them in your user cache (~/.rapidocr/ or C:\Users\<user>\.rapidocr\). 2) If you need strict offline operation, pre-download the models in a controlled environment and copy them to the target machine (or run the skill once while online). 3) Review and vet the rapidocr-onnxruntime package (source, maintainers, pinned version) before pip installing; consider pinning the dependency to a specific version. 4) Running the bundled tests or initializing the skill will trigger the dependency's download if models are not present. 5) If you require higher assurance, inspect the rapidocr-onnxruntime package code and its model download URL(s) or run the skill in an isolated environment/container.
功能分析
Type: OpenClaw Skill Name: rapid-ocr Version: 1.0.4 The RapidOCR skill is a legitimate OCR tool for processing invoices and train tickets. The code (rapidocr_minimal.py) uses standard regex for data extraction and relies on the well-known 'rapidocr-onnxruntime' library for OCR processing. The documentation (SKILL.md, TRANSPARENCY.md) is exceptionally transparent about the dependency's behavior of downloading model files (~13MB) on the first run, and the skill itself contains no evidence of data exfiltration, malicious execution, or prompt injection.
能力评估
Purpose & Capability
Name/description (ticket/invoice OCR) match the code and files. The sole external requirement is the rapidocr-onnxruntime dependency which is appropriate for on-device OCR and explains the model download behavior declared in multiple docs.
Instruction Scope
SKILL.md and README instruct typical usage (CLI/Python API) and explicitly warn that the dependency will download models on first run. The instructions do not ask the agent to read unrelated paths or secret env vars. Note: test_ocr.py and RapidOCRSkill.__init__ instantiate RapidOCR(), so running tests or initializing the skill will trigger the dependency's model download (network) unless models are already cached.
Install Mechanism
No install spec bundled; requirements.txt points to a PyPI package (rapidocr-onnxruntime). This is a standard, expected install mechanism (moderate trust surface). There are no downloads from unknown personal servers or URL shorteners in the skill files.
Credentials
No environment variables, credentials, or special config paths are requested. The skill writes model files to the user's cache directory (e.g., ~/.rapidocr/) on first run — this storage behavior is documented and proportionate to the task.
Persistence & Privilege
Skill does not request permanent 'always' inclusion, does not modify other skills or system-wide settings, and has no privileged persistence. The only persistent effect is storing downloaded model files in the user's cache directory, which is declared in the docs.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install rapid-ocr
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /rapid-ocr 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.4
- Clarified security details: added a section stating that the skill code itself does not make network requests, and outlining the behavior of the dependency during first use. - No code or functional changes; updated documentation only.
v1.0.3
- Added a new "重要说明" section clarifying initial network/model download requirements. - Separated and expanded details on network usage at each stage (installation, first run, later use). - Refined documentation: clarified core features, external dependencies, and model source/behavior. - Updated file structure documentation, including clarification of models/ folder content. - Adjusted API code examples for greater clarity and consistency. - SKILL.md now better communicates offline/online behavior and transparency.
v1.0.2
**Changelog v1.0.2** - Added all necessary OCR models and code for full offline functionality (models/*.onnx, rapidocr_minimal.py) - Introduced test_ocr.py for functional and integrity testing - Added documentation: TRANSPARENCY.md and an updated, more concise SKILL.md - Removed unused or legacy files: rapidocr_optimized.py, package.json, RELEASE_NOTES.md - All models now bundled internally, no download required for use
v1.0.1
✅ 新增火车票识别功能 ✅ 代码量:31KB → 40.8KB (+31%) ✅ 支持票据类型:15 种 → 20+ 种 ✅ 提取字段:30+ → 40+ 个 ✅ 测试覆盖率:2 张发票 → 2 张发票 + 1 张火车票
v1.0.0
Rapid-OCR 1.0.0 - Initial release - 支持增值税发票、火车票、飞机票、出租车票等多种票据的高精度 OCR 识别 - 基于 ONNX 推理引擎,完全离线运行,无需深度学习框架 - 提供结构化字段输出(30+ 关键字段)与通用文字识别 - 支持批量识别、数据校验(税号、金额、日期)与置信度评估 - 涵盖财务报销、企业票据管理、差旅、医疗、零售等常见场景
元数据
Slug rapid-ocr
版本 1.0.4
许可证 MIT-0
累计安装 12
当前安装数 11
历史版本数 5
常见问题

rapid ocr 是什么?

专业离线票据OCR工具,支持增值税发票、火车票、飞机票、出租车票等高精度字段提取与批量识别。 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 1147 次。

如何安装 rapid ocr?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install rapid-ocr」即可一键安装,无需额外配置。

rapid ocr 是免费的吗?

是的,rapid ocr 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

rapid ocr 支持哪些平台?

rapid ocr 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 rapid ocr?

由 gaojiren(@gaojiren)开发并维护,当前版本 v1.0.4。

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