AMuRD: Annotated Multilingual Receipts Dataset for Cross-lingual Key Information Extraction and Classification

by Abdelrahman Abdallah, Mahmoud Abdalla, Mohamed Elkasaby, Yasser Elbendary, Adam Jatowt

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.

Demo for our Instruct LLama

Explore our Instruct LLama system through our live demo:

Demo for our Instruct LLama

Examples

Example	Input	Class	Brand	Weight	Number of units	Size of units	Price	T.Price	Pack	Unit
Example 1	40.99 20.99 2 chunks sunshine	Tins, Jars & Packets	sunshine	No Weight	2	No Size of units	20.99	40.99	علبة	No Unit
Example 2	برسيل اتوماتيك جل روز 2.6	Cleaning Supplies	برسيل	2.6ل	1	No Size of units	No Price	No T.Price	عبوة	ل
Example 3	regina Pasta penne 400g	Rice, Pasta & Pulses	regina	400g	1	No Size of units	No Price	No T.Price	كيس	g
Example 4	10.00 400g Penne Pasta ElMaleka	Rice, Pasta & Pulses	ElMaleka	400g	1	No Size of units	10	10	كيس	g

Getting the code

To get started with the code and utilize the AMuRD dataset for your research or projects, you can clone this repository:

git clone https://github.com/yourusername/AMuRD.git

Dependencies

Reproducing the results

Citation

Please consider to cite our paper:

@misc{abdallah2023amurd,
    title={AMuRD: Annotated Multilingual Receipts Dataset for Cross-lingual Key Information Extraction and Classification},
    author={Abdelrahman Abdallah and Mahmoud Abdalla and Mohamed Elkasaby and Yasser Elbendary and Adam Jatowt},
    year={2023},
    eprint={2309.09800},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

License

Note: The AMuRD Dataset can only be used for non-commercial research purposes. For researchers who want to use the AMuRD database, please first fill in this Application Form and send it via email to us (m.abdallah@discoapp.ai, Yelbendary@discoapp.ai, abdoelsayed2016@gmail.com).