Enterprise document processing traditionally relies on OCR followed by rule-based extraction. Multimodal AI models that process both visual layout and text content simultaneously promise a step change in accuracy. We tested this hypothesis across three document types.
Experiment Scope
We evaluated multimodal document understanding on invoices, insurance claims, and scientific papers. Each document type presents different challenges: invoices have structured but variable layouts, claims combine forms with handwritten notes, and papers mix text, tables, figures, and equations.
Models Evaluated
We tested four approaches: traditional OCR + rules (Tesseract + regex), OCR + LLM (Tesseract + GPT-4), a vision-language model (GPT-4V), and a specialized document AI model (LayoutLMv3 fine-tuned on our data).
Evaluation Metrics
We measured field extraction accuracy (exact match), table extraction F1 score, processing time per document, and cost per document. Human annotators created gold-standard labels for 500 documents per type.
Results: Invoices
OCR + Rules: 78% field accuracy, 0.3s per document. OCR + LLM: 91% accuracy, 2.1s per document. GPT-4V: 94% accuracy, 3.8s per document. LayoutLMv3: 96% accuracy, 0.8s per document. The fine-tuned specialized model won on both accuracy and speed.
Results: Insurance Claims
OCR + Rules: 52% accuracy (struggled with handwriting). OCR + LLM: 73% accuracy. GPT-4V: 89% accuracy (handled handwriting well). LayoutLMv3: 82% accuracy. The general-purpose vision model outperformed the specialized model on this challenging format due to its superior handwriting recognition.
Results: Scientific Papers
Table extraction was the differentiating metric. OCR + Rules: 34% F1. OCR + LLM: 61% F1. GPT-4V: 78% F1. LayoutLMv3: 71% F1. Complex tables with merged cells and nested headers remained challenging for all approaches.
Key Takeaway
No single approach dominates all document types. The optimal strategy is a routing layer that classifies incoming documents and directs them to the best-performing pipeline. Specialized models win on high-volume, standardized formats. General-purpose multimodal models win on diverse, complex formats. The routing approach achieved 93% average accuracy across all three types while keeping costs manageable.
