# Cross-Lingual SynthDocs: A Large-Scale Synthetic Corpus for Any to Arabic OCR and Document Understanding

arXiv: https://arxiv.org/abs/2511.04699 · HTML: https://mlsanity.com/paper/2511.04699/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 1 Nov 2025.

## Builds on (most cited first, 12 of 12)

- [DocVQA: A Dataset for VQA on Document Images](https://mlsanity.com/paper/2007.00398/) (2020)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [Image-based table recognition: data, model, and evaluation](https://mlsanity.com/paper/1911.10683/) (2019)
- [Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval](https://mlsanity.com/paper/1502.07058/) (2015)
- [OCR-IDL: OCR Annotations for Industry Document Library Dataset](https://mlsanity.com/paper/2202.12985/) (2022)
- [DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis](https://mlsanity.com/paper/2107.02638/) (2021)
- [KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding](https://mlsanity.com/paper/2502.14949/) (2025)
- [S2 Chunking: A Hybrid Framework for Document Segmentation Through Integrated Spatial and Semantic Analysis](https://mlsanity.com/paper/2501.05485/) (2025)
- [SARD: A Large-Scale Synthetic Arabic OCR Dataset for Book-Style Text Recognition](https://mlsanity.com/paper/2505.24600/) (2025)
- [QARI-OCR: High-Fidelity Arabic Text Recognition through Multimodal Large Language Model Adaptation](https://mlsanity.com/paper/2506.02295/) (2025)
- [SynthDoc: Bilingual Documents Synthesis for Visual Document Understanding](https://mlsanity.com/paper/2408.14764/) (2024)
- [Arabic-Nougat: Fine-Tuning Vision Transformers for Arabic OCR and Markdown Extraction](https://mlsanity.com/paper/2411.17835/) (2024)

## Built on by (most cited first, 1 of 1)

- [How Far Can Synthetic Data Take Thai OCR?](https://mlsanity.com/paper/2609.03595/) (2026)

## Abstract (arXiv)

Cross-Lingual SynthDocs is a large-scale synthetic corpus designed to address the scarcity of Arabic resources for Optical Character Recognition (OCR) and Document Understanding (DU). The dataset comprises over 2.5 million of samples, including 1.5 million textual data, 270K fully annotated tables, and hundred thousands of real data based charts. Our pipeline leverages authentic scanned backgrounds, bilingual layouts, and diacritic aware fonts to capture the typographic and structural complexity of Arabic documents. In addition to text, the corpus includes variety of rendered styles for charts and tables. Finetuning Qwen-2.5-VL on SynthDocs yields consistent improvements in Word Error Rate (WER) and Character Error Rate (CER) in terms of OCR across multiple public Arabic benchmarks, Tree-Edit Distance Similarity (TEDS) and Chart Extraction Score (CharTeX) improved as well in other modalities. SynthDocs provides a scalable, visually realistic resource for advancing research in multilingual document analysis.