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Google Gemini OCR

The use of multimodal Gemini models to perform native vision-to-text extraction, converting complex document layouts, charts, and handwriting into structured Markdown or JSON for ingestion into RAG vector stores. Unlike traditional OCR, it leverages large-scale vision-language understanding to maintain semantic context and spatial relationships within the data.

Definition

The use of multimodal Gemini models to perform native vision-to-text extraction, converting complex document layouts, charts, and handwriting into structured Markdown or JSON for ingestion into RAG vector stores. Unlike traditional OCR, it leverages large-scale vision-language understanding to maintain semantic context and spatial relationships within the data.

Disambiguation

Unlike Google Document AI or Tesseract, Gemini OCR is 'model-native,' meaning the LLM itself 'sees' and interprets the image pixels directly rather than processing a pre-extracted text layer.

Visual Metaphor

"An expert analyst reading a complex architectural blueprint and describing every room's dimensions and purpose into a voice recorder, rather than just photocopy the lines."

Key Tools
Vertex AIGoogle AI StudioLangChain Multimodal LoadersLlamaIndex Multi-modal Indexing
Related Connections

Conceptual Overview

The use of multimodal Gemini models to perform native vision-to-text extraction, converting complex document layouts, charts, and handwriting into structured Markdown or JSON for ingestion into RAG vector stores. Unlike traditional OCR, it leverages large-scale vision-language understanding to maintain semantic context and spatial relationships within the data.

Disambiguation

Unlike Google Document AI or Tesseract, Gemini OCR is 'model-native,' meaning the LLM itself 'sees' and interprets the image pixels directly rather than processing a pre-extracted text layer.

Visual Analog

An expert analyst reading a complex architectural blueprint and describing every room's dimensions and purpose into a voice recorder, rather than just photocopy the lines.

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