Structured Data Extraction
Structured data extraction is a technique for using JSON-formatted prompts with Gemini to retrieve metadata and other information in a consistent, machine-readable format. By defining the expected output structure in JSON before sending a request, users can ensure that responses follow a predictable schema. This approach makes results easier to parse, validate, and integrate into automated workflows.
How It Works
The process involves specifying a JSON schema that describes the desired output format, then including this schema in the prompt sent to Gemini. The model understands these structured instructions and formats its response accordingly, rather than returning unstructured text.
Local Extraction Alternatives
For scenarios requiring privacy or offline capabilities, local models offer robust alternatives to cloud-based API extraction:
- TeleOCR: A 1.2 billion-parameter document parser developed by China Telecom’s AI research group. It is designed to accurately extract structured data from various document types, specifically handling “camera-captured” documents where traditional parsers often fail due to distortions, shadows, or angles.
- Efficiency: TeleOCR is notable for its ability to outperform larger models like GPT-5.2 on specific parsing tasks while running on hardware with as little as 8GB of VRAM.
- Integration: This tool complements cloud-based Gemini workflows by providing a local fallback for sensitive or high-volume document processing. See TeleOCR: Local 1.2B Model for Camera-Captured Document Parsing for detailed technical benchmarks.