A Production-Scale Deterministic Document Verification Framework Integrating OCR, Rule-Based Matching, and Intelligent Field Validation

Abstract

Optical character recognition and document-understanding models have improved extraction quality, yet enterprise workflows still fail when extracted values cannot be reliably validated, reconciled across documents, explained, or routed for review. This study proposes the Deterministic Production Verification Framework (DPVF), a post-extraction architecture combining semantic normalization, field-specific deterministic similarity, business-rule validation, cross-document consistency checks, confidence calibration, exception routing, and immutable audit traces. The executed experimental evaluation comprised 150,000 document cases grouped into 60,000 transaction bundles and compared OCR-only processing, OCR with generic fuzzy matching, OCR with machine-learning verification, and DPVF. On the held-out test partition, DPVF achieved 96.4% accuracy, precision of 0.968, recall of 0.955, and an F1-score of 0.961, with a 1.8% false-accept rate and 4.3% false-reject rate. It reduced manual review from 42.6% to 11.8%, a 72.3% relative decrease, while enabling 88.2% straight-through processing and 99.6% complete versioned audit traces. The largest ablation loss was 3.8 percentage points when verification was restricted to exact/edit-distance matching. DPVF remained accurate under severe corruption (91.9%), although XFUND was the hardest document stratum (94.8%). These findings establish experimental, not live-production, performance and support prospective validation in governed enterprise settings.