Extracting structured JSON from clean enterprise spreadsheets is solved technology, but real workflows run on multi-column PDF reports, scanned specification sheets, and unaligned data tables. We routed sixty varied document layouts through optical character recognition pipelines and specialized vision models to evaluate where autonomous extraction actually fails.
Merged Cells and Implicit Headers
The primary breakpoint across all parsers remains the merged table cell. When a header spans multiple sub-columns across page boundaries, vision-based parsers frequently misassign line items to adjacent columns without generating any processing warning. In financial tables with sparse data points, standard bounding-box extractors routinely grouped unrelated row figures into unified text blocks.
Vision Language Models Versus Layout Detectors
Dedicated layout detection engines paired with specialized OCR engines proved far more reliable for structured key-value pairs than feeding raw page images into general-purpose multimodal models. While multimodal models can draft plausible summaries, they introduce micro-hallucinations in numerical digits when the source resolution dips below three hundred dots per inch.
Building a Resilient Ingestion Gate
Before piping parsed outputs into an automated database, implement deterministic schema verification steps using basic type checking. Flagging missing row balances or altered column counts automatically prevents corrupt data from contaminating your primary analytical storage.