How should AI-assisted document entry separate reading, field extraction, validation and approval? This guide explains confidence scores, human review of exceptions and how to define a project scope.
For AI-assisted document processing, we recommend designing extraction, validation, review and registration as separate stages, rather than registering recognized text directly.Deciding which fields can be handled automatically and when to return them to a person makes validation and estimation more precise.
For companies reviewing entry of purchase orders, applications and similar documents, this article combines concepts from Google Cloud's public documentation with workflow examples from For f. It does not guarantee recognition accuracy or complete automation.
Separate reading text from using it as structured data
OCR and the organization of recognized text into dates, customers, products and quantities require different checks. Whether those values can be entered into a business system is another decision.
Google Cloud's Document AI document schema defines entity types, text and confidence values. Confidence is expressed on a scale from 0 to 1.Source: Google Cloud Document reference, Entity
Do not treat this value as a guaranteed probability of business correctness. Determine what can be registered automatically using actual documents and reference answers.
Define human review conditions for each field
| Stage | Processing | Example reasons for human review |
|---|---|---|
| Recognition | Read text from the document | Missing characters, incorrect orientation or poor image quality |
| Extraction | Identify required fields and values | Missing required fields or multiple candidates |
| Validation | Compare values with master data and cross-field rules | Customer not found, or quantity and other values outside expected conditions |
| Registration | Transfer reviewed data to the destination | Possible duplicate or an unresponsive destination |
For example, extracting a customer name does not determine which record to select if several customers share that name. Successful recognition alone should not trigger automatic processing.
Evaluate confidence thresholds against both errors and review volume
Document AI's evaluation documentation explains that raising the threshold generally increases precision while reducing recall. Predictions below the threshold are excluded from evaluation even when correct.Source: Google Cloud Evaluate performance, Confidence threshold
Request reporting on both incorrect values that pass through and cases sent for human review, rather than specifying a single threshold alone. Their relative importance depends on the field and business consequences.
This article does not propose a universal confidence threshold or accuracy target. Determine the conditions through testing for each document type.
Include difficult documents in the evaluation set
Cover the variation encountered in actual operations, rather than only well-formatted documents. List handwriting, customer-specific layouts, multiple pages, corrections and blank fields so the tested scope is clear.
- Assign someone to identify the required fields and verify reference values.
- Organize test documents and separate tuning examples from final evaluation examples.
- Record time spent on validation and correction, as well as recognition.
- Keep a record of unsupported formats without presenting them as supported.
For documents containing personal or commercial information, confirm what can be shared, where it will be stored and who can access it. Distinguish synthetic samples from actual business results.
Include exceptions and integration failures in the scope
A review queue can display the original document, extracted values and the reason review is required. Define who may correct and approve records, what happens next and whom to contact when processing stops.
Check duplicate handling for resubmitted documents and retry behavior after partial registration failures. Include manual fallback and processing history in the design so daily operations can continue.
Frequently asked questions
Should the project assume no human review?
First define the documents, fields and consequences of errors, then agree when review is required. Use the evaluation results to decide the automation scope.
Can we test OCR alone?
You can separate a small recognition and extraction trial from a broader integration with business systems. For f can support bothAI proof of conceptandModernization and integration.
FOR F / NEXT STEP
Talk to us about document recognition, review and integration.
For f helps define the scope, deliverables and approach around your current challenges. You can start with what you know, even if your documentation is incomplete.
References and verification date
Information checked on September 7, 2026. The public sources below inform this article; procurement guidance and examples are For f's explanations. Check current official documentation when using a product. The thumbnail is an AI-generated concept image, not an actual interface, document or measured result.