Why "Extraction" and "Verification" Are Different Problems in Document AI

Описание и применение

В статье описывается разница между извлечением и проверкой данных в автоматизации обработки счетов, а также пример внедрения решения iNextLabs DocsAI в компании Teck Sang, где разделение задач и исключения позволяют снизить ручную работу.

Задача

Teams often assume that extracting data from invoices automatically solves the problem of invoice processing, overlooking the distinct and harder task of verifying that extracted data is correct.

Результат

The separation of extraction and verification, coupled with exception-based processing, reduced manual review workload, increased consistency, and ensured that only truly anomalous invoices required human attention.

Рабочий процесс

• 1. Capture invoice document
• 2. Run OCR and parsing to extract structured fields
• 3. Retrieve relevant reference data (supplier agreements, historical prices)
• 4. Apply verification rules to compare extracted values against reference data
• 5. Flag documents with deviations as exceptions
• 6. Present flagged documents to human reviewers with context (rule, expected value, actual value, significance)

Доказательства

Extraction tells you what a document says. Verification tells you whether what it says is correct.

Источник

A system can have flawless extraction and still let bad invoices through, because extraction accuracy says nothing about whether the content of the document is actually right.

Источник