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ReceiptSense: Beyond Traditional OCR -- A Dataset for Receipt Understanding

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Abdelrahman Abdallah, Mohamed Mounis, Mahmoud Abdalla, Mahmoud SalahEldin Kasem, Mohamed Mahmoud, Ibrahim Abdelhalim, Mohamed Elkasaby, Yasser ElBendary
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Adam Jatowt

Computer Vision and Pattern Recognition · Submitted 6 Jun 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We establish baseline performance using traditional methods (Tesseract OCR) and advanced neural networks, demonstrating the dataset's effectiveness for processing complex, noisy real-world receipt layouts.

how it is stated · asserted 1.00 read the record →

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arXiv ID2406.04493 (version 2)
Pith IDJM5RXZDB, Pith's permanent record of this paper and its review
CitationAbdelrahman Abdallah, Adam Jatowt, Ibrahim Abdelhalim, Mahmoud Abdalla, Mahmoud SalahEldin Kasem, Mohamed Elkasaby, Mohamed Mahmoud, Mohamed Mounis, Yasser Elbendary. "ReceiptSense: Beyond Traditional OCR -- A Dataset for Receipt Understanding." arXiv:2406.04493 (2024). https://pith.science/paper/JM5RXZDB
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,388 input tokens.

What the abstract itself states, as probabilities: claims first 0.07 · states limitation 0.14 · human participants 0.12 · controlled comparison 0.77 · randomized assignment 0.01 · states quantitative result 0.86 · compares with prior results 0.84 · mentions code or data availability 0.95.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

Multilingual OCR and information extraction from receipts remains challenging, particularly for complex scripts like Arabic. We introduce \dataset, a comprehensive dataset designed for Arabic-English receipt understanding comprising 20,000 annotated receipts from diverse retail settings, 30,000 OCR-annotated images, and 10,000 item-level annotations, and a new Receipt QA subset with 1265 receipt images paired with 40 question-answer pairs each to support LLM evaluation for receipt understanding. The dataset captures merchant names, item descriptions, prices, receipt numbers, and dates to support object detection, OCR, and information extraction tasks. We establish baseline performance using traditional methods (Tesseract OCR) and advanced neural networks, demonstrating the dataset's effectiveness for processing complex, noisy real-world receipt layouts. Our publicly accessible dataset advances automated multilingual document processing research (see https://github.com/Update-For-Integrated-Business-AI/CORU ).

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Multilingual OCR and information extraction from receipts remains challenging, particularly for complex scripts like Arabic. background 0.96 0.04 0.04 asserted 1.00
s1 We introduce \dataset, a comprehensive dataset designed for Arabic-English receipt understanding comprising 20,000 annotated receipts from diverse retail settings, 30,000 OCR-annotated images, and 10,000 item-level annotations, and a new Receipt QA subset with 1265 receipt images paired with 40 question-answer pairs each to support LLM evaluation for receipt understanding. contribution 0.99 0.95 0.14 asserted 1.00
s2 The dataset captures merchant names, item descriptions, prices, receipt numbers, and dates to support object detection, OCR, and information extraction tasks. contribution 0.64 0.53 0.06 asserted 0.99
s3 We establish baseline performance using traditional methods (Tesseract OCR) and advanced neural networks, demonstrating the dataset's effectiveness for processing complex, noisy real-world receipt layouts. result 0.49 0.95 0.27 asserted 1.00
s4 Our publicly accessible dataset advances automated multilingual document processing research (see https://github.com/Update-For-Integrated-Business-AI/CORU ). contribution 0.63 0.51 0.32 asserted 1.00

What kind of paper the classifier reports this is: dataset or benchmark 1.00 · evidence from observation or experiment 0.96.

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sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Multilingual OCR and information extraction from receipts remains challenging, particularly for complex scripts like Arabic. We introduce \dataset, a comprehensive dataset designed for Arabic-English receipt understanding comprising 20,000 annotated receipts from diverse retail settings, 30,000 OCR-annotated images, and 10,000 item-level annotations, and a new Receipt QA subset with 1265 receipt images paired with 40 question-answer pairs each to support LLM evaluation for receipt understanding. The dataset captures merchant names, item descriptions, prices, receipt numbers, and dates to support object detection, OCR, and information extraction tasks. We establish baseline performance using traditional methods (Tesseract OCR) and advanced neural networks, demonstrating the dataset's effectiveness for processing complex, noisy real-world receipt layouts. Our publicly accessible dataset advances automated multilingual document processing research (see https://github.com/Update-For-Integrated-Business-AI/CORU ).

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References
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1 arXiv paper cite this in 2 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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What it cites · 1 sentence without a confident label

Cites as background 7 papers
  • Our evaluation spans a variety of model types, from convolutional neural networks (CNNs) [ref] and Transformers to cutting-edge language models, providing insights into their performance across different computational tasks and conditions outlined in the dataset.

    An Introduction to Convolutional Neural Networks · Models · 0.83

  • DETR [ref] the vision transformer model with Improved Denoising Anchor Boxes (DINO) [ref] employs a Transformer-based architecture that dynamically updates 4D anchor boxes through its decoder, using deformable attention to leverage multi-scale features effectively.

    DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection · Object Detection Models · 0.84

  • Zephyr [ref] [<https://huggingface.co/docs/transformers/main/model_doc/zephyr>] is a state-of-the-art model developed by the Hugging Face.

    Zephyr: Direct Distillation of LM Alignment · LLM models · 0.97

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