Large language models for optical character recognition lower error rates but produce stealthier hallucinations instead of obvious garbled text, rendering legacy validation tools obsolete and demanding new evaluation paradigms and system architectures.
The article delves into the technical challenges of extracting tabular data from documents, explaining that it's far more complex than standard text OCR and requires three core coordinated phases: detection, structure recognition, and data extraction.
LlamaIndex's blog post highlights that 90% of enterprise data is unstructured, and modern AI stacks (NLP, NER, LLM) can convert these documents into queryable structured information, unlocking significant business value.
Single-pass extraction fails silently on complex documents, while deep extraction uses an iterative, agent-driven verification loop to achieve near-perfect accuracy, making it essential for production workflows.