  The extraction of key information from receipts is a complex task that
involves the recognition and extraction of text from scanned receipts. This
process is crucial as it enables the retrieval of essential content and
organizing it into structured documents for easy access and analysis. In this
paper, we present AMuRD, a novel multilingual human-annotated dataset
specifically designed for information extraction from receipts. This dataset
comprises $47,720$ samples and addresses the key challenges in information
extraction and item classification - the two critical aspects of data analysis
in the retail industry. Each sample includes annotations for item names and
attributes such as price, brand, and more. This detailed annotation facilitates
a comprehensive understanding of each item on the receipt. Furthermore, the
dataset provides classification into $44$ distinct product categories. This
classification feature allows for a more organized and efficient analysis of
the items, enhancing the usability of the dataset for various applications. In
our study, we evaluated various language model architectures, e.g., by
fine-tuning LLaMA models on the AMuRD dataset. Our approach yielded exceptional
results, with an F1 score of 97.43\% and accuracy of 94.99\% in information
extraction and classification, and an even higher F1 score of 98.51\% and
accuracy of 97.06\% observed in specific tasks. The dataset and code are
publicly accessible for further
researchhttps://github.com/Update-For-Integrated-Business-AI/AMuRD.
