标题： QARI-OCR：通过多模态大型语言模型适应的高保真阿拉伯文文本识别 显示英文标题

标题： QARI-OCR: High-Fidelity Arabic Text Recognition through Multimodal Large Language Model Adaptation

摘要： 阿拉伯文书写系统的固有复杂性，包括其连笔特性、变音符号（塔什基尔）和多样的印刷字体，为光学字符识别（OCR）带来了持续的挑战。 我们提出了Qari-OCR，这是一系列基于Qwen2-VL-2B-Instruct的视觉-语言模型，通过在专门合成的数据集上进行迭代微调，逐步针对阿拉伯文进行了优化。 我们的领先模型QARI v0.2，在带有变音符号的文本上实现了新的开源最先进水平，其字错误率（WER）为0.160，字符错误率（CER）为0.061，BLEU得分为0.737。 Qari-OCR展示了在处理塔什基尔、多种字体以及文档布局方面的卓越能力，并且在低分辨率图像上表现出色。 进一步的研究（QARI v0.3）显示了结构化文档理解和手写文本的强大潜力。 这项工作显著提高了阿拉伯文OCR的准确性和效率，并且所有模型和数据集均已公开发布，以促进进一步的研究。 显示英文摘要

摘要： The inherent complexities of Arabic script; its cursive nature, diacritical marks (tashkeel), and varied typography, pose persistent challenges for Optical Character Recognition (OCR). We present Qari-OCR, a series of vision-language models derived from Qwen2-VL-2B-Instruct, progressively optimized for Arabic through iterative fine-tuning on specialized synthetic datasets. Our leading model, QARI v0.2, establishes a new open-source state-of-the-art with a Word Error Rate (WER) of 0.160, Character Error Rate (CER) of 0.061, and BLEU score of 0.737 on diacritically-rich texts. Qari-OCR demonstrates superior handling of tashkeel, diverse fonts, and document layouts, alongside impressive performance on low-resolution images. Further explorations (QARI v0.3) showcase strong potential for structural document understanding and handwritten text. This work delivers a marked improvement in Arabic OCR accuracy and efficiency, with all models and datasets released to foster further research.