A sophisticated AI-powered tool that leverages Google Gemini to perform high-accuracy OCR on scanned or image-based PDF documents.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install geminipdfocr
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install geminipdfocr using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is PDF OCR using Gemini LLM?
The PDF OCR using Gemini LLM skill is designed to bridge the gap between static, image-based documents and actionable text data. By utilizing Google's advanced multimodal vision capabilities, this Openclaw Skills extension provides superior text extraction compared to traditional OCR engines, making it ideal for processing complex layouts, multi-page documents, and scanned archives. It allows developers and AI agents to convert non-selectable PDF content into clean, structured data for further analysis or search indexing.
This skill is particularly effective when integrated into automated workflows that require understanding the context of a document rather than just reading raw characters. As part of the growing ecosystem of Openclaw Skills, it offers a robust solution for developers needing reliable document digitisation within their AI-driven applications.
PDF OCR using Gemini LLM Use Cases
- Digitizing old scanned archives into searchable text formats.
- Extracting data from invoices, receipts, and forms where text is embedded in images.
- Preparing unstructured PDF data for RAG (Retrieval-Augmented Generation) pipelines.
- Converting image-based legal documents into markdown or JSON for automated analysis.
How PDF OCR using Gemini LLM Works
- The user provides a path to a local PDF file and an optional output destination.
- The system splits the PDF into individual page images to ensure optimal processing by the vision model.
- Each page is uploaded to the Google Gemini API using the provided credentials.
- The Gemini LLM performs visual analysis and OCR to extract text from each page.
- The tool aggregates the extracted content and returns it as a plain text file or a structured JSON object.
PDF OCR using Gemini LLM Setup
To get started with this skill, ensure you have a Google API Key. Follow these steps to prepare your environment:
cd geminipdfocr
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
export GOOGLE_API_KEY=your-actual-api-key
Once installed, you can run the OCR process using the following command:
python -m geminipdfocr <path-to-pdf> --json --output result.json
PDF OCR using Gemini LLM Data Schema & Taxonomy
The skill manages document data through a streamlined pipeline, utilizing Python-based dependencies for file handling and schema validation.
| Component | Type | Description |
|---|---|---|
| Input | File Path | One or more valid paths to PDF documents. |
| Output Format | Text/JSON | Extracted text stream or structured JSON data. |
| Dependencies | UV Packages | google-genai, pymupdf, pydantic, pydantic-settings. |
| Processing | Page-wise | Documents are split into single-page files before API transmission. |
All metadata and configuration are handled via pydantic-settings to ensure environment variables like the API key are correctly validated before execution.
PDF OCR using Gemini LLM Advanced Features
- Structured JSON Support: Use the --json flag to receive output in a machine-readable format for downstream automation.
- Page Limitation: The --max-pages N flag allows for testing or partial document processing to manage API costs.
- Quiet Execution: Suppress verbose progress logs using the --quiet flag for clean integration into CI/CD pipelines.
- High-Fidelity OCR: Leverages Gemini's multimodal vision model, which outperforms standard OCR in recognizing complex document structures.
- Seamless Integration: Designed to work natively within the Openclaw Skills framework for AI coding agents.
SKILL.md
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