Document Intelligence

Document intelligence extracts structure and meaning from files such as invoices, receipts, contracts, forms, and reports. A reliable pipeline covers classification → OCR/layout → field/table extraction → validation → human review → learning. Results depend on input quality, well-defined schemas, and measured confidence.

Overview

Sources arrive as PDFs, images (TIFF/JPEG/PNG), or digital forms. Digital PDFs may contain selectable text; scanned PDFs require OCR. Layout varies widely (fixed forms vs. semi-structured vs. free-form). Good programs define document types and fields, enforce validation rules, and route low-confidence cases to human review with feedback loops for model improvement.

Where it fits

Finance

Invoices, receipts, statements: key-value extraction, three-way match support, tax and currency normalization.

Operations & logistics

BOLs, delivery notes, meter runs, lab certificates: tables and units; tolerance checks and chain-of-custody fields.

Legal & compliance

Contracts, consents, ID docs: clause/party extraction, signature dates, policy conformance checks.

Pipeline

Stages

  1. Ingest & classify (by type, language, sensitivity)
  2. OCR & layout analysis (text lines, blocks, tables, key regions)
  3. Field/table extraction with model ensembles
  4. Validation (schema, business rules, cross-doc checks)
  5. Human-in-the-loop for low-confidence/edge cases
  6. Feedback & model improvement

Design notes

  • Separate document storage from runtime context; keep hashes and provenance
  • Idempotent processing with retry; page-level checkpoints
  • Queue-based scaling; per-type model routing

OCR & layout

OCR methods

  • Traditional OCR (e.g., Tesseract): binarization, de-skew, segmentation, language packs
  • Neural OCR (transformer-based) for complex fonts/handwriting

Layout analysis

  • Detect text blocks, lines, tables, and key-value anchors
  • Use document structure for better field alignment

References

Fields, tables & entities

Approaches

  • Template rules for fixed forms (fast; fragile to variation)
  • ML models for semi-structured forms (learned anchors)
  • LLM-assisted extraction with schema validation

Tables

  • Detect table boundaries, headers, and row merges
  • Reconstruct line items; compute totals; cross-check sums

Normalization

  • Dates, currencies, units, tax rates
  • Reference data lookups (vendors, materials, GL codes)

Confidence, thresholds & human-in-the-loop

Confidence design

  • Per-field confidence; minimum thresholds by field importance
  • Outlier rules (e.g., totals vs. sum of lines; tax validity)

HITL

  • Route low-confidence fields to reviewers with highlights and tooltips
  • Capture corrections; feed back to training sets

Good practice

  • Separate “unknown” from “confident zero” values
  • Keep audit trails of edits (user, timestamp, old/new)

Schema, validation & normalization

Schema

  • Define fields, types, ranges, and requiredness per doc type
  • Map to system-of-record entities and codes

Validation

  • Syntactic checks (regex/enums), referential checks (master data), and cross-field checks

Localization

  • Multiple languages and date/currency formats
  • Tax, address, and ID formats by jurisdiction

Evaluation & labeling

Metrics

  • OCR: character/word error rate (CER/WER)
  • Field extraction: precision, recall, F1 per field
  • Table extraction: cell accuracy, line-item F1

Labeling

  • Gold sets with inter-annotator agreement (IAA)
  • Active learning: sample uncertain items; re-train periodically

Test discipline

  • Holdout sets by layout/source; measure drift over time
  • Track cost-to-correct and reviewer throughput

Privacy & security

Data handling

  • PII/PHI redaction where required; encrypt at rest/in transit
  • Retention by policy; delete originals if policy allows
  • Isolate training datasets from production unless consented

Access & audit

  • Role-based access; approvals for sensitive types (IDs, health)
  • Immutable logs: who viewed/edited which fields

Architecture patterns

Async queues

  • Batch ingestion; per-page jobs; retries on transient errors
  • Idempotency keys; poison-queue handling

Model routing

  • Per-type/document model selection; language routing
  • Fallback to general model when type unknown

Synchronous paths

  • Small docs and low-latency flows (e.g., ticket classification)
  • Timeouts with graceful downgrade to queue

90-day starter

Days 0–30

  • Pick one document type; define schema and rules
  • Assemble 200–500 sample docs; label a gold set

Days 31–60

  • Stand up OCR/layout + extraction; set confidence thresholds
  • Implement HITL; log corrections with reasons

Days 61–90

  • Evaluate CER/WER and F1; target one weak field
  • Normalize to system-of-record; plan scale-out

References

Extract facts with confidence—and prove them with evidence.

If you want a schema template, threshold guide, and a HITL checklist, ask for a copy.

Contact us