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

TL;DR

Cross-Lingual SynthDocs is a large-scale synthetic Arabic document corpus that enhances OCR and document understanding by providing diverse, realistic data for training and benchmarking models.

Contribution

The paper introduces SynthDocs, a comprehensive synthetic Arabic document dataset with over 2.5 million samples, improving OCR and document understanding performance.

Findings

Finetuning Qwen-2.5-VL improves OCR accuracy on Arabic benchmarks.

SynthDocs enhances multi-modal document analysis metrics.

The dataset supports scalable research in multilingual document processing.

Abstract

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.