2024/06/06 by Abdelrahman Abdallah, Abdallah, Abdelrahman, Mahmoud Abdalla +14
Computer Science · #Artificial intelligence #Computation and Language (cs.CL) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #FOS: Computer and information sciences #Natural Language Processing Techniques #Natural language processing #Parsing #Receipt #World Wide Web
paper · pdf · doi:10.48550/arxiv.2406.04493
openalex publication_date 2024/06/06 · openalex created_date 2024/06/11 · openalex updated_date 2026/07/28
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 ).