A table that's quietly misaligned. A paragraph that's silently dropped. Ever had a document parse that looked right but wasn't?
Title: OCR Is Dead. Long Live Agentic OCR
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Drawing on lessons from building LlamaParse, LlamaIndex lays out what it means to treat document parsing as agentic: the system decides how to read a document, calls tools or specialized models as needed, and changes approach based on results. Three highlights stand out.
๐งฉ Adaptive reading strategy
Instead of processing every element uniformly, resources are allocated based on document complexity โ tables get specialized attention while simpler text gets standard handling.
๐ Evidence-based self-correction
Simply reprocessing a problematic section isn't enough. The system has to verify, with checkable evidence, that a correction actually improves accuracy without sacrificing fidelity.
โฑ๏ธ Clear limits and objectives
Explicit constraints on time and cost coexist with a single goal: faithfully reconstructing the entire document, down to headers, footers, and other page furniture.
What stands out here is the bar itself moving โ once AI agents rely on parsed documents for consequential work, "readable" isn't enough; it has to be faithfully reconstructed.
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