Abstract
Arabic Optical Character Recognition (OCR) is essential for converting vast amounts of Arabic print media into digital formats. However, training modern OCR models, especially powerful vision-language models, is hampered by the lack of large, diverse, and well-structured datasets that mimic real-world book layouts. Existing Arabic OCR datasets often focus on isolated words or lines or are limited in scale, typographic variety, or structural complexity found in books. To address this significant gap, we introduce SARD (Large-Scale Synthetic Arabic OCR Dataset). SARD is a massive, synthetically generated dataset specifically designed to simulate book-style documents. It comprises 843,622 document images containing 690 million words, rendered across ten distinct Arabic fonts to ensure broad typographic coverage. Unlike datasets derived from scanned documents, SARD is free from real-world noise and distortions, offering a clean and controlled environment for model training. Its synthetic nature provides unparalleled scalability and allows for precise control over layout and content variation. We detail the dataset's composition and generation process and provide benchmark results for several OCR models, including traditional and deep learning approaches, highlighting the challenges and opportunities presented by this dataset. SARD serves as a valuable resource for developing and evaluating robust OCR and vision-language models capable of processing diverse Arabic book-style texts.
Abstract (translated)
阿拉伯光学字符识别（OCR）对于将大量阿拉伯印刷媒体转换为数字格式至关重要。然而，训练现代OCR模型，尤其是强大的视觉语言模型，由于缺乏大规模、多样且结构良好的数据集来模拟真实世界的书籍布局而受到阻碍。现有的阿拉伯语OCR数据集往往集中于孤立的单词或行，或者在规模、字体变化或书籍中发现的结构复杂性方面存在局限性。为了解决这一重要缺口，我们介绍了SARD（大规模合成阿拉伯语OCR数据集）。SARD是一个巨大的、由合成生成的数据集，专门设计用于模拟书本风格的文档。它包含843,622张文件图像，其中包含了以十种不同的阿拉伯字体呈现的6.9亿个单词，以确保广泛的排版覆盖率。与从扫描文档中提取的数据集不同，SARD不受现实世界噪声和失真的影响，为模型训练提供了干净且受控的环境。其合成特性提供了前所未有的可扩展性，并允许对布局和内容变化进行精确控制。我们详细介绍了该数据集的组成及生成过程，并提供了几种OCR模型（包括传统方法和深度学习方法）的基准测试结果，突出展示了此数据集带来的挑战与机遇。SARD为开发和评估能够处理多样化阿拉伯书本风格文本的强大OCR和视觉语言模型提供了宝贵的资源。
URL
https://arxiv.org/abs/2505.24600
