Document AI
InteractiveDocument OCR Pipeline (Deep OCR)
Turn a scan into structured JSON: layout, OCR, post-processing.
ocrdocument-processinglayout-analysisextraction
Explanation
Document processing is the practical side of OCR: not just “read text”, but extract *structure* (tables, key-value pairs, totals, dates) reliably enough to automate workflows.
Typical pipeline
- Preprocess: deskew, denoise, normalize contrast.
- Layout analysis: find regions (header/table/footer) and reading order.
- OCR: recognize text in each region (often with per-word confidence).
- Post-processing: correct errors, validate formats, map text → a schema.
“Deep OCR” in plain terms
Newer OCR systems combine stronger vision encoders with better sequence decoding, which helps with noisy scans, mixed fonts, and complex layouts. Many document stacks also add an LLM-based step for schema mapping and correction.
What to look for
- Errors are rarely uniform: totals and IDs are brittle.
- Layout is as important as recognition for tables.
- Good systems expose confidence and let you route low-confidence fields to review.
Document Processing (OCR)
Adjust scan quality and see how an OCR pipeline turns a document into structured data.
error
0.08overall 92%Controls
Presets
Pipeline
Step Output
OCR
OCR returns raw text (and often word/line bounding boxes + confidences). Here we simulate typical character-level mistakes.
Before → After
accuracy
100.0%correct 296/296 High Good Low PoorBelow threshold:
�ACME Supplies Inc. Invoice #: INV-10492 Date: 2025-11-30 Bill To: Max Mustermann Ship To: Max Mustermann Items 1 Notebook (A5) 3 x 6.50 = 19.50 2 Pen (Blue) 5 x 1.20 = 6.00 3 Sticky Notes 2 x 2.75 = 5.50 Subtotal: 31.00 Tax (8%): 2.48 Total: 33.48