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README.md

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-# [CORU: Comprehensive Post-OCR Parsing and Receipt Understanding Dataset]()
-
-In the fields of Optical Character Recognition (OCR) and Natural Language Processing (NLP), integrating multilingual capabilities remains a critical challenge, especially when considering languages with complex scripts such as Arabic. This paper introduces the Comprehensive Post-OCR Parsing and Receipt Understanding Dataset (CORU), a novel dataset specifically designed to enhance OCR and information extraction from receipts in multilingual contexts involving Arabic and English. CORU consists of over 20,000 annotated receipts from diverse retail settings in Egypt, including supermarkets and clothing stores, alongside 30,000 annotated images for OCR that were utilized to recognize each detected line, and 10,000 items annotated for detailed information extraction. These annotations capture essential details such as merchant names, item descriptions, total prices, receipt numbers, and dates. They are structured to support three primary computational tasks: object detection, OCR, and information extraction. We establish the baseline performance for a range of models on CORU to evaluate the effectiveness of traditional methods, like Tesseract OCR, and more advanced neural network-based approaches. These baselines are crucial for processing the complex and noisy document layouts typical of real-world receipts and for advancing the state of automated multilingual document processing.
-## Dataset Overview
-
-CORU is divided into Three challenges:
-
-- **Key Information Detection.**
-- **Large-Scale OCR Dataset**
-- **Item Information Extraction**
-### Dataset Statistics
-
-| Category             | Training | Validation | Test  |
-|----------------------|----------|------------|-------|
-| Object Detection     | 12,600   | 3700       | 3700  |
-| OCR                  | 21,000   | 4,500      | 4,500 |
-| IE                   |  7000    | 1500       | 1500  |
-## Sample Images from the Dataset
-
-Here are five examples from the dataset, showcasing the variety of receipts included:
-
-<img src="images/1.jpg" alt="Sample Image 1" width="200" height="300" align="left"> 
-<img src="images/2.jpg" alt="Sample Image 2" width="200" height="300" align="left"> 
-<img src="images/3.jpg" alt="Sample Image 3" width="200" height="300" align="left"> 
-<br clear="left">
-
-## Download Links
-### Key Information Detection
-- **Training Set**:  [Download](https://huggingface.co/datasets/abdoelsayed/CORU/resolve/main/Receipt/train.zip?download=true)
-- **Validation Set**: [Download](https://huggingface.co/datasets/abdoelsayed/CORU/resolve/main/Receipt/val.zip?download=true)
-- **Test Set**: [Download](https://huggingface.co/datasets/abdoelsayed/CORU/resolve/main/Receipt/test.zip?download=true)
-### OCR Dataset
-- **Training Set**: [Download](https://huggingface.co/datasets/abdoelsayed/CORU/resolve/main/OCR/train.zip?download=true)
-- **Validation Set**: [Download](https://huggingface.co/datasets/abdoelsayed/CORU/resolve/main/OCR/val.zip?download=true)
-- **Test Set**: [Download](https://huggingface.co/datasets/abdoelsayed/CORU/resolve/main/OCR/test.zip?download=true)
-### Item Information Extraction
-- **Training Set**: [Download](https://huggingface.co/datasets/abdoelsayed/CORU/resolve/main/IE/train.csv?download=true)
-- **Validation Set**: [Download](https://huggingface.co/datasets/abdoelsayed/CORU/resolve/main/IE/val.csv?download=true)
-- **Test Set**: [Download](https://huggingface.co/datasets/abdoelsayed/CORU/resolve/main/IE/test.csv?download=true)
-## Citation
-
-If you find these codes or data useful, please consider citing our paper as:
+# 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
 ```
-@misc{abdallah2024coru,
-    title={CORU: Comprehensive Post-OCR Parsing and Receipt Understanding Dataset},
-    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},
+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},
-    eprint={2406.04493},
-    archivePrefix={arXiv},
-    primaryClass={cs.CV}
+    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**: [[email protected]](mailto:[email protected])
+
+---
+
+<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>