Optical Character Recognition OCR
Optical Character Recognition (OCR) is a technology that converts images of text, tables, and documents into machine-readable text formats. In the context of AI agents and retrieval-augmented generation (RAG) systems, OCR serves as a critical preprocessing step that enables the extraction and indexing of information from unstructured visual documents. By transforming visual content into structured text, OCR allows these systems to access and process information that would otherwise remain inaccessible to language models and search algorithms.
Role in RAG Systems
In retrieval-augmented generation workflows, OCR acts as a bridge between document images and knowledge bases. When documents arrive as scanned PDFs, photographs, or other image formats, OCR extracts the underlying text content, making it available for indexing and retrieval. This extracted text can then be chunked, embedded, and stored in vector databases or traditional search indices, enabling RAG systems to retrieve relevant information when answering user queries. Without OCR, these visual documents would be invisible to the retrieval pipeline.
Specialized Models for Tables
Open-source OCR models tailored for table extraction have become particularly important for RAG applications, as tables contain structured data that requires precise spatial recognition to maintain relationships between cells and columns. These specialized models can identify table structures and convert them into formats suitable for retrieval and generation tasks, preserving the semantic meaning of tabular information rather than simply linearizing it into raw text.
Source Notes
- 2026-04-07: Google Gemma 4 Open Weight Models Apache 20 and Enhanced AI · ▶ source
- 2026-04-08: LiteParse Free Local Layout Preserving Document Parsing for LLMs · ▶ source
- 2026-04-21: Google DeepMind
- 2026-04-22: Google Gemma · ▶ source