Mdpbench Eval

---
name: mdpbench-eval
description: MDPBench evaluates the capability of document parsing models to accurately extract text, formulas, tables, and layout structures from multilingual document images under real-world conditions. It specifically probes robustness to photographic degradation, non-Latin scripts, right-to-left reading orders, and cross-lingual generalization without prior language or image-type knowledge. Use when the user wants to benchmark on MDPBench, or asks about evaluating this task. Reports accuracy.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2603.28130
  bibtex_key: [REDACTED]
  confidence: high
---

# mdpbench-eval

> MDPBench: A Benchmark for Multilingual Document Parsing in Real-World Scenarios — Zhang Li et al. (2026) (arXiv:2603.28130, 2026)

## What this evaluates

MDPBench evaluates the capability of document parsing models to accurately extract text, formulas, tables, and layout structures from multilingual document images under real-world conditions. It specifically probes robustness to photographic degradation, non-Latin scripts, right-to-left reading orders, and cross-lingual generalization without prior language or image-type knowledge.

## Datasets

- **MDPBench** — total 3400; splits: test (3400); repo https://github.com/Yuliang-Liu/MultimodalOCR

## Metrics

- `accuracy` **(primary)** — range: percent
  - Percentage of correctly parsed documents out of the total evaluated. Higher is better.
- `Edit` — range: other
  - Edit distance between predicted and ground-truth text strings. Lower is better.
- `CDM` — range: [0, 1]
  - Formula recognition metric (Character-level Document Match or similar). Higher is better.
- `TEDS` — range: [0, 1]
  - Tree Edit Distance based Similarity for table structure recognition. Higher is better.
- `PageIoU` — range: [0, 1]
  - Page Intersection over Union for layout detection. Higher is better.

## Input / output format

**Input**: Document images (digital-born or photographed) without metadata indicating language or capture type.

**Output**: Parsed document representation including text content, layout bounding boxes, and recognized formulas/tables.

## Scoring recipe

```python
def compute_accuracy(predictions, gold):
    correct = sum(1 for p, g in zip(predictions, gold) if p == g)
    return (correct / len(gold)) * 100

# Component metrics use standard implementations:
# Edit: levenshtein_distance(pred_text, gold_text)
# CDM: character-level match score for formulas
# TEDS: tree_edit_distance_similarity(pred_table, gold_table)
# PageIoU: intersection_over_union(pred_layout, gold_layout)
```

## Common pitfalls

- Models are evaluated without prior knowledge of the input language or whether the image is photographed or digital; providing this metadata artificially inflates performance.
- Component-level evaluations (e.g., cropped text blocks) favor models trained on block-level data, while end-to-end full-page evaluations favor models trained on full-document images, leading to inconsistent rankings across settings.
- Right-to-left scripts (e.g., Arabic) are often misparsed due to left-to-right processing assumptions in standard OCR pipelines.

## Evidence (verbatim from paper)

> As shown in Tab.[2], the top-performing proprietary model, Gemini-3-Pro*[gemini3pro]*, achieves an overall accuracy of 86.4%, reaching state-of-the-art (SOTA) results in 14 of 17 languages. In contrast, the best open-source model, dots.mocr, attains 80.5% overall accuracy, revealing a clear gap between proprietary and open-source approaches.

## Citation

```bibtex
@misc{zhang2026mdpbench,
  title={MDPBench: A Benchmark for Multilingual Document Parsing in Real-World Scenarios},
  author={Zhang Li et al. (2026)},
  year={2026},
  note={arXiv:2603.28130}
}
```

- arXiv: 2603.28130