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- W4386876024 abstract "Key information extraction involves recognizing and extracting text from scanned receipts, enabling retrieval of essential content, and organizing it into structured documents. This paper presents a novel multilingual dataset for receipt extraction, addressing key challenges in information extraction and item classification. The dataset comprises $47,720$ samples, including annotations for item names, attributes like (price, brand, etc.), and classification into $44$ product categories. We introduce the InstructLLaMA approach, achieving an F1 score of $0.76$ and an accuracy of $0.68$ for key information extraction and item classification. We provide code, datasets, and checkpoints.footnote{url{https://github.com/Update-For-Integrated-Business-AI/AMuRD}}." @default.
- W4386876024 created "2023-09-20" @default.
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- W4386876024 date "2023-09-18" @default.
- W4386876024 modified "2023-09-27" @default.
- W4386876024 title "AMuRD: Annotated Multilingual Receipts Dataset for Cross-lingual Key Information Extraction and Classification" @default.
- W4386876024 doi "https://doi.org/10.48550/arxiv.2309.09800" @default.
- W4386876024 hasPublicationYear "2023" @default.
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