Image Parsing
Image parsing is a document processing technique that extracts text and structural information from document images while preserving layout and spatial relationships. Unlike traditional optical character recognition (OCR), which focuses primarily on converting visual text into machine-readable form, image parsing maintains formatting details, document structure, and the relative positions of elements on a page. This preservation of layout information enables large language models to better understand document context and hierarchy.
Applications with Language Models
Image parsing has become increasingly relevant in AI agent workflows where documents serve as input sources. By retaining spatial relationships—such as column arrangements, table structures, and element positioning—parsed documents can be more effectively processed by large language models. This is particularly valuable for complex documents like forms, tables, invoices, and multi-column layouts where semantic meaning depends on arrangement rather than text order alone.
Technical Considerations
Effective image parsing typically involves multiple processing steps: initial image analysis to identify structural elements, text extraction with position coordinates, and classification of document regions by type or function. The output format must balance completeness with usability, representing layout information in ways that language models can process efficiently while remaining human-interpretable.
Source Notes
- 2026-04-08: Stop using paid APIs for document parsing (Here’s what to use instead)