Title: ReceiptSense: Beyond Traditional OCR -- A Dataset for Receipt Understanding Show Chinese title

Title: ReceiptSense：超越传统OCR——用于收据理解的数据集

Abstract: Multilingual OCR and information extraction from receipts remains challenging, particularly for complex scripts like Arabic. We introduce \dataset, a comprehensive dataset designed for Arabic-English receipt understanding comprising 20,000 annotated receipts from diverse retail settings, 30,000 OCR-annotated images, and 10,000 item-level annotations, and a new Receipt QA subset with 1265 receipt images paired with 40 question-answer pairs each to support LLM evaluation for receipt understanding. The dataset captures merchant names, item descriptions, prices, receipt numbers, and dates to support object detection, OCR, and information extraction tasks. We establish baseline performance using traditional methods (Tesseract OCR) and advanced neural networks, demonstrating the dataset's effectiveness for processing complex, noisy real-world receipt layouts. Our publicly accessible dataset advances automated multilingual document processing research (see https://github.com/Update-For-Integrated-Business-AI/CORU ). Show Chinese abstract

Abstract: 多语言OCR和收据信息提取仍然具有挑战性，特别是对于阿拉伯语等复杂脚本。我们引入了\dataset ，这是一个为阿拉伯语-英语收据理解设计的全面数据集，包含来自不同零售环境的20000个注释收据，30000个OCR注释图像和10000个项目级注释，并有一个新的收据QA子集，包含1265张收据图像，每张图像配有40对问题和答案，以支持收据理解的大型语言模型评估。该数据集记录了商家名称、物品描述、价格、收据编号和日期，以支持目标检测、OCR和信息提取任务。我们使用传统方法（Tesseract OCR）和先进的神经网络建立了基线性能，证明了该数据集在处理复杂、嘈杂的现实世界收据布局方面的有效性。我们公开可访问的数据集推动了自动化多语言文档处理研究（见https://github.com/Update-For-Integrated-Business-AI/CORU）。