Duplicate from abdoelsayed/CORU

Co-authored-by: Abdelrahman Abdallah <abdoelsayed@users.noreply.huggingface.co>

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README.md

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+---
+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
+
+[![Paper](https://img.shields.io/badge/Paper-arXiv-red)](https://arxiv.org/abs/2406.04493)
+[![Dataset](https://img.shields.io/badge/Dataset-HuggingFace-yellow)](https://huggingface.co/datasets/abdoelsayed/CORU)
+[![License](https://img.shields.io/badge/License-MIT-blue)]()
+
+## 🔥 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
+
+<div align="center">
+
+| Sample 1 | Sample 2 | Sample 3 | Sample 4 | Sample 5 |
+|----------|----------|----------|----------|----------|
+| <img src="images/0cf392e3-e6bf-4bd7-85d5-7f91c73cdcaf.jpg" width="150" height="200"> | <img src="images/0dccefa6-6928-499e-8aae-15c04d18cc94.jpg" width="150" height="200"> | <img src="images/0dd4ada2-681e-42e7-b398-e093bc8b81c3.jpg" width="150" height="200"> | <img src="images/0ef51dc7-4a0a-47e6-bc59-41f609d1c98d.jpg" width="150" height="200"> | <img src="images/0f369dc1-1c5b-41b1-97bc-c9b94d53cd40.jpg" width="150" height="200"> |
+
+*Examples of annotated receipt images showcasing the variety of formats, languages, and complex text layouts*
+
+</div>
+
+## 🎯 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)](https://huggingface.co/datasets/abdoelsayed/CORU/resolve/main/Receipt/train.zip?download=true)
+- **Validation Set**: [Download (3.7K images)](https://huggingface.co/datasets/abdoelsayed/CORU/resolve/main/Receipt/val.zip?download=true)
+- **Test Set**: [Download (3.7K images)](https://huggingface.co/datasets/abdoelsayed/CORU/resolve/main/Receipt/test.zip?download=true)
+
+### 🔍 OCR Dataset
+- **Training Set**: [Download (21K images)](https://huggingface.co/datasets/abdoelsayed/CORU/resolve/main/OCR/train.zip?download=true)
+- **Validation Set**: [Download (4.5K images)](https://huggingface.co/datasets/abdoelsayed/CORU/resolve/main/OCR/val.zip?download=true)
+- **Test Set**: [Download (4.5K images)](https://huggingface.co/datasets/abdoelsayed/CORU/resolve/main/OCR/test.zip?download=true)
+
+### 📝 Item Information Extraction
+- **Training Set**: [Download (7K items)](https://huggingface.co/datasets/abdoelsayed/CORU/resolve/main/IE/train.csv?download=true)
+- **Validation Set**: [Download (1.5K items)](https://huggingface.co/datasets/abdoelsayed/CORU/resolve/main/IE/val.csv?download=true)
+- **Test Set**: [Download (1.5K items)](https://huggingface.co/datasets/abdoelsayed/CORU/resolve/main/IE/test.csv?download=true)
+
+### ❓ Receipt Question Answering
+- **Test Set**: [Download (1,265 receipts with 50.6K QA pairs)](https://huggingface.co/datasets/abdoelsayed/CORU/resolve/main/QA/test.zip?download=true)
+
+> ⚠️ **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
+```python
+# 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:
+
+```bibtex
+@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](LICENSE) file for details.
+
+## 🔗 Links
+
+- 📄 **Paper**: [arXiv:2406.04493](https://arxiv.org/abs/2406.04493)
+- 🤗 **HuggingFace**: [abdoelsayed/CORU](https://huggingface.co/datasets/abdoelsayed/CORU)
+- 💼 **DISCO App**: [https://discoapp.ai/](https://discoapp.ai/)
+- 📧 **Contact**: [abdelrahman.abdallah@uibk.ac.at](mailto:abdelrahman.abdallah@uibk.ac.at)
+
+---
+
+<div align="center">
+
+**🌟 Star this repository if you find it helpful! 🌟**
+
+![Visitors](https://api.visitorbadge.io/api/visitors?path=https%3A%2F%2Fgithub.com%2FUpdate-For-Integrated-Business-AI%2FCORU&countColor=%23263759)
+
+</div>

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