Multimodal Evaluation
Rigorous testing for vision-language models and document pipelines. Measure OCR fidelity, complex table cell extraction, financial chart interpretation, and visual evidence grounding.
What We Evaluate
Real-world enterprise documents are messy: PDFs with multi-column flows, skewed smartphone scans, nested tables with merged header cells, and complex presentation charts. We test whether vision-language models associate numbers with correct row/column headers without hallucinating spurious data.
The Cost of Visual Hallucinations
When an OCR or multimodal model encounters a blurred digit or merged table cell, standard models often interpolate a plausible number rather than expressing uncertainty. In financial and medical document pipelines, these silent numeric errors represent major regulatory liabilities.
| Metric | Dimension | Description |
|---|---|---|
| Cell Value Extraction Accuracy | TABLE OCR | Exact string and numeric precision when extracting cells across complex tabular grids. |
| Cross-Column Association Fidelity | LAYOUT | Accuracy in mapping values to correct hierarchical parent headers in merged-cell tables. |
| Chart Trend & Axis Reasoning | VISUAL QA | Correct interpretation of non-linear axis scaling, bar chart magnitudes, and legend keys. |
| Spurious Digit Interpolation | SAFETY | Frequency of hallucinating digits on low-contrast, noisy, or unreadable document segments. |
| Document Complexity | Sample Slides | Extraction Accuracy | Observed Failure Mode |
|---|---|---|---|
| Standard Bordered Tables | 200 | 97.4% | Nominal document performance |
| Merged Multi-Row Subheaders | 120 | 82.4% | Misattributed row values across categories |
| Dual-Axis Financial Charts | 80 | 76.5% | Confused secondary percentage axis with dollar values |
Benchmark Your Vision Pipelines
Test OCR pipelines, document analyzers, and multimodal models on realistic domain records under mutual NDA.