ReceiptQA: A Question-Answering Dataset for Receipt Understanding

Mahmoud Abdalla, Mahmoud SalahEldin Kasem, Mohamed Mahmoud, Bilel Yagoub, Mostafa Farouk Senussi, Abdelrahman Abdallah, Seung Hun Kang and Hyun Soo Kang ()
Additional contact information
Mahmoud Abdalla: Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea
Mahmoud SalahEldin Kasem: Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea
Mohamed Mahmoud: Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea
Bilel Yagoub: Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea
Mostafa Farouk Senussi: Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea
Abdelrahman Abdallah: Information Technology Department, Faculty of Computers and Information, Assiut University, Assiut 71526, Egypt
Seung Hun Kang: Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea
Hyun Soo Kang: Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Republic of Korea

Mathematics, 2025, vol. 13, issue 11, 1-20

Abstract: Understanding information extracted from receipts is a critical task for real-world applications such as financial tracking, auditing, and enterprise resource management. In this paper, we introduce ReceiptQA , a novel large-scale dataset designed for receipt understanding through question-answering (QA). ReceiptQA contains 171,000 question–answer pairs derived from 3500 receipt images, constructed via two complementary methodologies: (1) LLM-Generated Dataset: 70,000 synthetically generated QA pairs, where each receipt is paired with 20 unique, context-specific questions. These questions are produced using a state-of-the-art large language model (LLM) and validated through human annotation to ensure accuracy, relevance, and diversity. (2) Human-Created Dataset: 101,000 manually crafted questions spanning answerable and unanswerable queries. This subset includes carefully designed templates of varying difficulty (easy/hard) to comprehensively evaluate QA systems across diverse receipt domains. To benchmark performance, we evaluate leading vision–language models (VLMs) and language models (LMs), including GPT-4o, Phi-3B, Phi-3.5B, LLaVA-7B, InternVL2 (4B/8B), LLaMA-3.2, and Gemini. We further fine-tune a LLaMA-3.2 11B model on ReceiptQA , achieving significant improvements over baseline models on validation and test sets. Our analysis uncovers critical strengths and limitations of existing models in handling receipt-based QA tasks, establishing a robust benchmark for future research.

Keywords: receipt understanding; question answering; vision-language models; large language models; multimodal learning; semi-structured data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/11/1760/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/11/1760/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:11:p:1760-:d:1664584

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().