Datasets:
license: mit
task_categories:
- object-detection
- text-classification
- zero-shot-classification
language:
- en
- ar
size_categories:
- 10K<n<100K
ReceiptSense: Beyond Traditional OCR - A Dataset for Receipt Understanding
🔥 News
- [2024] ReceiptSense dataset is now publicly available!
- [2024] Paper accepted and published
📖 Abstract
Multilingual OCR and information extraction from receipts remains challenging, particularly for complex scripts like Arabic. We introduce ReceiptSense, a comprehensive dataset designed for Arabic-English receipt understanding comprising:
- 20,000 annotated receipts from diverse retail settings
- 30,000 OCR-annotated images
- 10,000 item-level annotations
- 1,265 receipt images with 40 question-answer pairs each for Receipt QA
The dataset captures merchant names, item descriptions, prices, receipt numbers, and dates to support object detection, OCR, information extraction, and question-answering 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.
🎯 Key Features
✨ Multilingual Support
- Arabic-English bilingual receipts
- Real-world mixed-language content
- Complex script handling for Arabic text
📊 Comprehensive Annotations
- Object Detection: Bounding boxes for key receipt elements
- OCR: Character and word-level text recognition
- Information Extraction: Structured data extraction
- Receipt QA: Question-answering capabilities
🏪 Diverse Retail Environments
- Supermarkets and grocery stores
- Restaurants and cafes
- Clothing and retail shops
- Various geographical regions
🔧 Real-world Challenges
- Noisy and degraded image quality
- Complex receipt layouts
- Mixed fonts and orientations
- Authentic retail scenarios
📈 Dataset Statistics
| Component | Training | Validation | Test | Total |
|---|---|---|---|---|
| Key Information Detection | 12,600 | 3,700 | 3,700 | 20,000 |
| OCR Dataset | 21,000 | 4,500 | 4,500 | 30,000 |
| Item Information Extraction | 7,000 | 1,500 | 1,500 | 10,000 |
| Receipt QA | - | - | 1,265 | 1,265 |
Language Distribution
- Arabic: 53.6%
- English: 26.2%
- Mixed Language: 20.3%
Receipt QA Coverage
- Merchant/Payment/Date Metadata: 30%
- Item-level Information: 50%
- Tax/Total/Payment Details: 20%
🖼️ Sample Images
| Sample 1 | Sample 2 | Sample 3 | Sample 4 | Sample 5 |
|---|---|---|---|---|
 |
 |
 |
 |
 |
Examples of annotated receipt images showcasing the variety of formats, languages, and complex text layouts
🎯 Supported Tasks
1. 🎯 Key Information Detection
Extract essential receipt information including:
- Merchant names
- Transaction dates
- Receipt numbers
- Item lists and descriptions
- Total amounts
2. 🔍 OCR (Optical Character Recognition)
Box-level text annotations for:
- Multilingual text recognition
- Complex layout understanding
- Noisy image processing
3. 📝 Information Extraction
Detailed item-level analysis:
- Item names and descriptions
- Prices and quantities
- Categories and classifications
- Brands and packaging information
4. ❓ Receipt Question Answering
Comprehensive QA capabilities covering:
- Receipt metadata queries
- Item-specific questions
- Transaction summary questions
- Payment and tax information
📥 Download Links
🎯 Key Information Detection
- Training Set: Download (12.6K images)
- Validation Set: Download (3.7K images)
- Test Set: Download (3.7K images)
🔍 OCR Dataset
- Training Set: Download (21K images)
- Validation Set: Download (4.5K images)
- Test Set: Download (4.5K images)
📝 Item Information Extraction
- Training Set: Download (7K items)
- Validation Set: Download (1.5K items)
- Test Set: Download (1.5K items)
❓ Receipt Question Answering
⚠️ Note: All receipt datasets have been updated to include PII-redacted versions for privacy protection.
🏆 Baseline Results
Object Detection Performance
| Model | Backbone | Precision | Recall | mAP50 | mAP50-95 |
|---|---|---|---|---|---|
| YOLOv7 | - | 76.0% | 85.6% | 79.2% | 43.7% |
| YOLOv8 | - | 74.6% | 81.0% | 76.1% | 45.3% |
| YOLOv9 | - | 75.7% | 83.4% | 77.9% | 46.7% |
| DINO | Swin-T | - | - | - | 32.2% (Avg IoU) |
OCR Performance
| Model | CER ↓ | WER ↓ |
|---|---|---|
| Tesseract | 15.56% | 30.78% |
| Attention-Gated CNN-BiGRU | 14.85% | 27.22% |
| Our OCR Model | 7.83% | 27.24% |
| Azura OCR | 6.39% | 25.97% |
Receipt QA Performance
| Model | Precision | Recall | Exact Match | Contains |
|---|---|---|---|---|
| GPT-4o | 37.7% | 36.4% | 35.0% | 29.1% |
| Llama3.2 (11B) | 32.6% | 31.3% | 31.6% | 25.9% |
| Phi3.5 | 28.4% | 29.1% | 28.8% | 23.7% |
| Internvl2 (8B) | 24.2% | 23.8% | 23.1% | 19.4% |
🚀 Getting Started
Quick Start
# Install required packages
pip install datasets transformers torch
# Load the dataset
from datasets import load_dataset
# Load Receipt QA dataset
qa_dataset = load_dataset("abdoelsayed/CORU", "qa")
# Load OCR dataset
ocr_dataset = load_dataset("abdoelsayed/CORU", "ocr")
# Load Information Extraction dataset
ie_dataset = load_dataset("abdoelsayed/CORU", "ie")
Dataset Structure
ReceiptSense/
├── Receipt/ # Key Information Detection
│ ├── images/ # Receipt images
│ └── annotations/ # YOLO/COCO format annotations
├── OCR/ # OCR Dataset
│ ├── images/ # Text line images
│ └── labels/ # Character annotations
├── IE/ # Information Extraction
│ └── data.csv # Structured item data
└── QA/ # Receipt Question Anshwering
├── images/ # Receipt images
└── qa_pairs.json # Question-answer pairs
🔬 Applications
- 💳 Expense Management: Automated expense tracking and categorization
- 📦 Inventory Management: Real-time inventory updates from receipt data
- 🏪 Retail Analytics: Customer behavior and purchasing pattern analysis
- 🤖 Document AI: Multilingual document understanding systems
- 📱 Mobile Apps: Receipt scanning and digitization applications
🤝 Comparison with Existing Datasets
| Dataset | Images | Categories | Languages | Item IE | Receipt QA | Year |
|---|---|---|---|---|---|---|
| SROIE | 1,000 | 4 | English | ✓ | ✗ | 2019 |
| CORD | 1,000 | 8 | English | ✓ | ✗ | 2019 |
| MC-OCR | 2,436 | 4 | EN + Vietnamese | ✓ | ✗ | 2021 |
| UIT | 2,147 | 4 | EN + Vietnamese | ✓ | ✗ | 2022 |
| ReceiptSense | 20,000 | 5 | Arabic + English | ✓ | ✓ | 2024 |
🏛️ Ethics and Privacy
- All receipts collected with explicit user consent through the DISCO application
- Comprehensive 4-step PII redaction process implemented
- Privacy protocols strictly followed during data collection
- Independent verification and cross-checking procedures
👥 Authors
Abdelrahman Abdallah¹, Mahmoud Abdalla², Mahmoud SalahEldin Kasem², Mohamed Mahmoud², Ibrahim Abdelhalim³, Mohamed Elkasaby⁴, Yasser Elbendary⁴, Adam Jatowt¹
¹University of Innsbruck, Innsbruck, Tyrol, Austria
²Chungbuk National University, Cheongju, Republic of Korea
³University of Louisville, Louisville, USA
⁴DISCO, Cairo, Egypt
📚 Citation
If you find ReceiptSense useful for your research, please consider citing our paper:
@article{abdallah2024receiptsense,
title={ReceiptSense: Beyond Traditional OCR - A Dataset for Receipt Understanding},
author={Abdelrahman Abdallah and Mahmoud Abdalla and Mahmoud SalahEldin Kasem and Mohamed Mahmoud and Ibrahim Abdelhalim and Mohamed Elkasaby and Yasser Elbendary and Adam Jatowt},
year={2024},
journal={ACM Conference Proceedings},
note={Comprehensive multilingual receipt understanding dataset}
}
📄 License
This dataset is released under the MIT License. See LICENSE file for details.
🔗 Links
- 📄 Paper: arXiv:2406.04493
- 🤗 HuggingFace: abdoelsayed/CORU
- 💼 DISCO App: https://discoapp.ai/
- 📧 Contact: [email protected]
🌟 Star this repository if you find it helpful! 🌟