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Layout-preserving OCR vs plain text dumps
Why bounding boxes, reading order, and table structure matter more than a wall of characters when you export or embed documents.
Most OCR pipelines still treat a page as a bag of characters. You get a long string, maybe with newlines, and hope downstream systems can guess where tables, columns, and captions lived. That works for keyword search. It fails for review UIs, searchable PDF export, and structure-aware chunking.
Layout-preserving OCR keeps the geometry: bounding boxes per block, reading order, and table cells. When you open a job in OCRs.ai you see text sitting on the same regions as the source — not a skeleton of gray bars pretending to be content.
What you can do with layout that plain text cannot
Export searchable PDF and DOCX that still look like the invoice or contract you uploaded.
Highlight a missed line during QC and jump straight to that bbox on the page.
Chunk by section and table for embeddings instead of arbitrary token windows that split mid-row.
When plain text is enough
If you only need grep-style search over scanned letters, a dump is fine. The moment humans verify output — or you push to a vector DB for RAG — layout becomes the product, not a nice-to-have.
Try layout OCR with AI QC on OCRs.ai
Free credits at signup — then metered pages and video.