OCR

Drop an invoice and watch text regions get detected and read, field by field.

Simulated

Text recognition

Layout detection
OCR
Post-processing

globex-invoice-0482.pdfReady

Globex SuppliesInvoice1200 Harbor Way, Suite 400Springfield, OR 97477billing@globex.exampleInvoice no.INV-2026-0482Issue dateSep 22, 2026Due dateOct 22, 2026PO numberACME-PO-10482Bill toShip toAcme CorporationAttn: Accounts Payable42 Industrial Park RdRiverton, CA 94016Acme Warehouse 388 Dockside AvePort Elena, CA 94022DescriptionQtyUnit priceAmountIndustrial fasteners, M8 (box of 200)4$38.50$154.00Hydraulic hose assembly, 2 m6$72.00$432.00Nitrile safety gloves (case)10$24.90$249.00Pallet wrap film, 18 in12$16.75$201.00Payment terms: Net 30Bank transfer to account ending 7310Subtotal$1,036.00Tax (8.25%)$85.47Total due$1,121.47Approved – J.R.Questions about this invoice? Contact billing@globex.example

Header
Text
Key-value
Table row
Total

Bounding boxes

Extracted text

Regions

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Avg confidence

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Processing time

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No text extracted yet

Run OCR to find every text region on the page, read it line by line and score how sure the model is.

How it works in production

The demo above runs on a script in your browser. This is the architecture it stands in for.

Intake

Upload or inbox

Pre-processing

Deskew, denoise, split pages

Recognition

Layout detection

Text recognition

Table structure

Post-processing

Normalisation

Dates, amounts, IDs

Confidence scoring

Output

Structured JSON

Review queue

OCR pipeline

Scans are cleaned up first (rotation, contrast, page splitting), then a layout model finds regions such as headers, key-value pairs and table cells before text is read. Reading by region keeps line items in the right rows.

Every line carries a confidence score. Values are normalised (dates, currency, invoice numbers) and anything under the threshold is flagged for a person instead of flowing silently into downstream systems.

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