Document Classification with OCR

Visual Flow

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When to Use This Pattern

Use this pattern whenever documents flow into your process and a human has to look at each one just to decide where it belongs:

• Accounts payable: separating invoices from credit notes from statements
• Legal intake: classifying contracts, NDAs, amendments, correspondence
• HR: sorting CVs, references, certifications, ID scans
• Compliance: tagging documents by retention requirement

The triage is the bottleneck, not the work that follows. Automating the triage frees the humans for the judgment calls.

How It Works

Three stages:

1. OCR — extract text from the document. For clean PDFs with embedded text, just read it. For scans or images, run an OCR engine (AWS Textract, Google Document AI, Tesseract for self-hosted).
2. Entity extraction — pull structured fields: invoice_number, total_amount, counterparty_name, effective_date. Most commercial OCR services bundle this.
3. Classification — assign a document type. Rule-based if the keywords are reliable ("Invoice" in the header). ML-based (a classifier trained on past labelled docs) when rules get too brittle.

Route each document based on its class plus any extracted fields (e.g. invoice with total > $10K goes to tier-2 AP).

Implementation Guide

Step 1: Measure baseline human accuracy

Before automating, know what you're replacing. If humans are right 97% of the time and the model is 93%, you've made things worse. Sample a week of decisions; label them; compare.

Step 2: Pick OCR output shape for your data

Forms benefit from structured OCR (AWS Textract's forms, Google's Form Parser). Free-text documents want plain extraction. Don't pay for structured OCR where you don't need it.

Step 3: Decide: rules or model, or both

Rules: fast to start, brittle when formats change. Model: slower to start (needs labelled data), robust to variation. Hybrid is common — rules handle the obvious cases, model handles the rest, human handles low-confidence.

Step 4: Build the human-review queue from day one

No production doc pipeline runs at 100% accuracy. Plan a review queue for low-confidence items. Route human corrections back as labelled training data.

Step 5: Track accuracy in production

Periodically sample outcomes and manually verify. Drift happens — a supplier changes their invoice layout and your model quietly starts misclassifying. Monitoring is the safety net.

Tips & Best Practices

• Store originals forever. Extracted data can be re-derived; lost originals can't.
• Redact PII before sending to external classifiers unless you've cleared it with legal.
• Version the classifier. "The model changed last Tuesday" is a real debugging clue.
• Route rejections, don't drop them. An unknown-type document going silently to /dev/null is a customer-impact bug waiting to happen.
• Start small. One doc type at a time. Getting invoices right is already valuable; trying to do 15 types at once fails.