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Document Processing

Receipt Capture and OCR Processing for Finance Teams

Expense management handled systematically — so you spend time on decisions, not on chasing data.

Duration 4 weeks
Level intermediate
Read 7 min
Receipt Capture and OCR Processing for Finance Teams
Program price ₪2,400
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Program structure

Each stage builds directly on the previous one. The sequence is deliberate — skipping ahead tends to create gaps that show up later.

intermediate

Implementation Stages

  • Stage 1 — Receipt type inventory

    Catalog all receipt formats your team currently submits: digital, paper scans, email PDFs.

  • Stage 2 — OCR tool configuration

    Set extraction fields, confidence thresholds, and language/currency parameters.

  • Stage 3 — Exception queue design

    Build a review workflow for receipts that fall below the confidence threshold.

  • Stage 4 — Accuracy testing

    Process a batch of 200 historical receipts and measure field-level extraction accuracy.

  • Stage 5 — Production rollout

    Deploy with a two-week monitoring period before removing manual backup processes.

What this covers in practice

The weakest point in most expense systems is the moment a receipt enters the process. Paper, photos, PDFs, and email forwards all arrive differently, and manual entry at that stage is where errors accumulate.

What OCR actually does in this context

Optical character recognition reads receipt images and extracts vendor name, date, amount, and currency. The extracted data then populates expense fields automatically, without manual typing.

Accuracy varies by receipt quality. Crumpled paper receipts, low-light photos, and handwritten totals consistently produce extraction errors that require a fallback review step.

Tools and integrations covered

  • Veryfi API for high-volume receipt processing
  • AutoEntry for accountant-side batch uploads
  • Microsoft Azure Form Recognizer for custom document types
  • Mobile capture via Expensify and SAP Concur apps

Where this service focuses effort

Configuration of extraction rules, confidence thresholds, and rejection queues takes up most of the implementation time. The goal is a system where at least 88% of receipts process without human review — not 100%, which is not realistic across all document types.

Exception handling is built in from the start, not added later when problems appear.