KITAB_pdf_to_markdown_reviewed (Corrected KITAB-Bench PDF→Markdown)

Short description. A carefully reviewed and corrected version of the KITAB-Bench PDF-to-Markdown subset for Arabic document OCR evaluation. We fixed ground-truth errors (hallucinated text, missing page numbers, omissions of small-font text) and standardized formatting to provide a reliable benchmark for model comparison.

TL;DR

• ✅ Human-verified ground truth for Arabic PDF→Markdown
• ✅ Removes hallucinations and fills missing/omitted content
• ✅ Keeps the original task and schema for drop-in evaluation
• 🔗 Based on KITAB-Bench

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Motivation & Background

Evaluating Arabic OCR and document understanding models requires robust, accurate benchmarks. During an assessment of the original KITAB-Bench PDF-to-Markdown subset[^kitab], we found problems that compromise fair evaluation:

• Hallucinated ground truth: some reference markdown contained phrases not present in the source page (likely tool-generated). Example: one entry included the English sentence:

  “**You're right - let me write it exactly as it appears in the image, maintaining the right-to-left direction:**”

• Missing page numbers in references.
• Omission of small-font text that is clearly visible in the source image.

To address this, we manually reviewed and corrected the ground truth, producing this dataset.

What’s in this dataset?

• Split: train
• Records: currently ~60+ page-level samples (may grow in future versions).
• Fields:
  • image — the page image.
  • markdown — human-verified, structure-preserving Markdown for the page.

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How we corrected the data

1. Removed hallucinated phrases that do not appear in the image.
2. Restored omitted content, including small-font text.
3. Added/verified page markers when appropriate.
4. Normalized minor formatting to keep the task consistent across samples.

Our goal was minimal, faithful correction: keep the original task and layout intent, while ensuring the ground truth actually matches the page.

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Usage

from datasets import load_dataset

ds = load_dataset("Misraj/KITAB_pdf_to_markdown_reviewed", split="train")
row = ds[0]

# image preview
row["image"].show()

# markdown preview
print(row["markdown"][:800])

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Evaluation protocol (suggested)

Commonly reported metrics for this task include:

• WER / CER — word/character error rate (↓ better)
• BLEU / ChrF — text similarity (↑ better)
• TEDS — structural fidelity of tree/HTML/Markdown (↑ better)
• MARS — combined structure + text score (↑ better)

Evaluate text metrics on normalized text; compute TEDS/MARS on rendered trees/blocks to reflect layout/structure preservation.

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Example results (on the corrected KITAB-Bench PDF→Markdown)

Snapshot from our experiments using only open-source models for fairness; best in bold, second-best underlined.

Model	WER ↓	CER ↓	BLEU ↑	CHRF ↑	TEDS ↑	MARS ↑
Dots.ocr	0.39	0.28	59.28	83.16	43	63.08
Baseer (ours)	0.61	0.40	55.78	80.26	56	68.13
Nanonets	0.51	0.40	51.37	77.45	33	55.225
Qari	0.65	0.48	44.61	71.45	43	57.225
Qwen2.5-VL-3B	0.70	0.57	40.44	66.78	31	48.89
Qwen2.5-VL-7B	0.76	0.63	36.76	62.45	24	43.225
Gemma3-12B	0.85	0.69	27.56	52.09	55	53.545
Gemma3-4B	0.95	0.82	12.94	31.72	27	29.36
Aya-vision	1.27	0.96	5.58	16.19	26	21.095
AIN	1.18	1.08	2.61	3.99	24	13.995

Reading the snapshot. Dots.ocr leads most text-centric metrics, while Baseer achieves the best structural score (TEDS) and best overall MARS, reflecting stronger layout understanding. The KITAB-Bench subset is small (tens of pages), so each misprediction impacts the score noticeably. On our larger and more diverse Misraj-DocOCR benchmark (400 expert-verified pages), Baseer’s advantage is more pronounced.

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How to cite

If you use this dataset, please cite both this corrected release and the original KITAB-Bench:

This dataset (recommended):

@misc{hennara2025baseervisionlanguagemodelarabic,
      title={Baseer: A Vision-Language Model for Arabic Document-to-Markdown OCR}, 
      author={Khalil Hennara and Muhammad Hreden and Mohamed Motasim Hamed and Ahmad Bastati and Zeina Aldallal and Sara Chrouf and Safwan AlModhayan},
      year={2025},
      eprint={2509.18174},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2509.18174}, 
}