Understanding Extraction Accuracy
Accuracy is the most important metric in document extraction — but also one of the most misunderstood. Many vendors report character-level accuracy (CER), which measures how well text is recognized from images. For business applications, what matters is field-level accuracy: does the system extract the right value for the right field from each document?
Typical Accuracy Benchmarks
Based on enterprise document processing deployments, agentic extraction systems achieve:
- Digital invoices (native PDF): 97-99% field-level accuracy
- Scanned invoices (good quality): 94-98% field-level accuracy
- Contracts (standard commercial): 94-97% on key term extraction
- Medical records (printed): 92-96% field-level accuracy
- Handwritten forms: 85-92% field-level accuracy
- Multi-language documents: 90-96% depending on language
Factors That Affect Accuracy
Document quality is the largest factor affecting extraction accuracy. Low-resolution scans, heavy compression artifacts, skewed pages, and low-contrast printing all degrade accuracy. Implementing document quality checks at intake is the single most effective accuracy improvement for scan-heavy workflows.
Measuring and Monitoring Extraction Accuracy
Ongoing accuracy monitoring requires a sampling strategy. Review a random sample of processed documents weekly to detect accuracy degradation before it affects downstream processes. Most enterprise extraction platforms provide built-in accuracy dashboards showing exception rates and field-level accuracy by document type.
Continuous Improvement with Papirus AI
Papirus AI incorporates continuous learning from human review corrections. Most deployments see 20-40% reduction in exception rates within the first 90 days as the system adapts to each customer’s specific document formats and vocabulary.
