CrossLingR: A Comprehensive Multilingual Receipt Dataset for Cross-Language Information Extraction and Classification

The process of key information extraction is critical for converting scanned receipts into structured, accessible documents, facilitating the efficient retrieval of vital data. This research introduces an expansive, novel multilingual dataset designed to propel advancements in the domain of receipt information extraction and item classification. Our dataset encompasses 47,720 annotated samples, detailed with item names, associated attributes such as price and brand, and organized into 44 distinct product categories. We unveil the InstructLLaMA methodology, a pioneering approach that demonstrates significant effectiveness, evidenced by an F1 score of 0.76 and an accuracy of 0.68 in tasks of key information extraction and item classification. To support further research and application development, we make available our comprehensive dataset, the InstructLLaMA model, and relevant resources at https://github.com/Update-For-Integrated-Business-AI/AMuRD.