  In the fields of Optical Character Recognition (OCR) and Natural Language
Processing (NLP), integrating multilingual capabilities remains a critical
challenge, especially when considering languages with complex scripts such as
Arabic. This paper introduces the Comprehensive Post-OCR Parsing and Receipt
Understanding Dataset (CORU), a novel dataset specifically designed to enhance
OCR and information extraction from receipts in multilingual contexts involving
Arabic and English. CORU consists of over 20,000 annotated receipts from
diverse retail settings, including supermarkets and clothing stores, alongside
30,000 annotated images for OCR that were utilized to recognize each detected
line, and 10,000 items annotated for detailed information extraction. These
annotations capture essential details such as merchant names, item
descriptions, total prices, receipt numbers, and dates. They are structured to
support three primary computational tasks: object detection, OCR, and
information extraction. We establish the baseline performance for a range of
models on CORU to evaluate the effectiveness of traditional methods, like
Tesseract OCR, and more advanced neural network-based approaches. These
baselines are crucial for processing the complex and noisy document layouts
typical of real-world receipts and for advancing the state of automated
multilingual document processing. Our datasets are publicly accessible
(https://github.com/Update-For-Integrated-Business-AI/CORU).
