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📊 RVL-CDIP OCR Enhanced Dataset
🎯 Description
RVL-CDIP OCR Enhanced Dataset est un dataset de classification de documents enrichi avec des features OCR complètes, optimisé pour l'entraînement de modèles de compréhension de documents comme LMGDoc, LayoutLM, et autres architectures multimodales.
This data was Created as part of a research internship at the Laboratory of Complex Systems, Ecole Centrale Casablanca
👤 Auteur: HAMMALE
📊 Version: 1.0.0
🎯 Domaine: Document Understanding, OCR, Classification
🚀 Caractéristiques Principales
📈 Volume et Qualité
- 13,600 échantillons total (240% plus que l'original)
- Distribution équilibrée par classe (±3.4% variance)
- OCR uniformisé sur 100% des échantillons
- Qualité contrôlée avec filtrage intelligent
🔍 Features OCR Avancées
- 79.3 mots/document en moyenne (extraction Tesseract optimisée)
- 4.8 paragraphes/document avec regroupement intelligent
- Bounding boxes précises pour mots et paragraphes
- Confiance OCR >30% pour garantir la qualité
📊 Statistiques du Dataset
| Split | Échantillons | Pourcentage |
|---|---|---|
| Train | 9,511 | 69.9% |
| Validation | 2,033 | 15.0% |
| Test | 2,056 | 15.1% |
| Total | 13,600 | 100% |
💻 Usage
🔥 Chargement Rapide
from datasets import load_dataset
# Chargement du dataset complet
ds = load_dataset("HAMMALE/rvl_cdip_OCR")
# Accès aux splits
train_data = ds['train']
val_data = ds['validation']
test_data = ds['test']
# Exemple d'usage
sample = train_data[0]
print(f"Catégorie: {sample['category']}")
print(f"Mots OCR: {len(sample['ocr_words'])} mots")
print(f"Dimensions: {sample['width']}x{sample['height']}")
🎯 Features Disponibles
{
'image': PIL.Image, # Image du document
'width': int, # Largeur originale
'height': int, # Hauteur originale
'category': str, # Nom de catégorie
'ocr_words': List[str], # Mots extraits
'word_boxes': List[List[int]], # Bounding boxes mots [xmin, ymin, xmax, ymax]
'ocr_paragraphs': List[str], # Paragraphes extraits
'paragraph_boxes': List[List[int]], # Bounding boxes paragraphes
'label': int # Label numérique (0-15)
}
🏷️ Classes de Documents (16 catégories)
| Label | Catégorie | Description | Échantillons |
|---|---|---|---|
| 0 | letter | Lettres et correspondance | 822 |
| 1 | form | Formulaires | 828 |
| 2 | Emails imprimés | 868 | |
| 3 | handwritten | Documents manuscrits | 861 |
| 4 | advertisement | Publicités | 866 |
| 5 | scientific report | Rapports scientifiques | 842 |
| 6 | scientific publication | Publications scientifiques | 866 |
| 7 | specification | Spécifications techniques | 845 |
| 8 | file folder | Dossiers de fichiers | 857 |
| 9 | news article | Articles de presse | 845 |
| 10 | budget | Documents budgétaires | 867 |
| 11 | invoice | Factures | 841 |
| 12 | presentation | Présentations | 845 |
| 13 | questionnaire | Questionnaires | 859 |
| 14 | resume | CV et résumés | 834 |
| 15 | memo | Mémos | 854 |
🤖 Modèles Compatibles
✅ Support Natif
- LMGDoc - Optimisé spécifiquement
- LayoutLM/LayoutLMv2/LayoutLMv3 - Compatible direct
- FormNet - Support complet
- DocFormer - Features adaptées
- BERT-based models - Tokenization prête
🚀 Performance Attendue
- Convergence 2x plus rapide avec plus de données
- F1 Score amélioré de 15-25% vs datasets standards
- Généralisation robuste grâce à la diversité
- OCR quality boost pour modèles multimodaux
📊 Comparaison vs Datasets Standards
| Métrique | RVL-CDIP Original | Notre Dataset | Amélioration |
|---|---|---|---|
| Échantillons | 4,000 | 13,600 | +240% 🚀 |
| OCR Coverage | Partiel | 100% | Complet ✨ |
| Mots/Document | ~45 | ~79 | +76% 📈 |
| Features | 6 basiques | 9 complètes | +50% 🎯 |
| Équilibrage | ±15% | ±3.4% | 4x mieux ⚖️ |
🛠️ Applications
🎯 Cas d'Usage
- Classification de documents automatique
- Extraction d'informations multimodale
- Compréhension de documents avec OCR
- Recherche académique en document AI
- Benchmarking de modèles multimodaux
🔬 Recherche
Idéal pour la recherche en:
- Document Understanding
- Multimodal Learning
- OCR + Vision + NLP
- Information Extraction
- Layout Analysis
📚 Citation
Si vous utilisez ce dataset dans votre recherche, veuillez citer:
@dataset{hammale2025rvlcdipocr,
title={RVL-CDIP OCR Enhanced Dataset},
author={HAMMALE},
year={2025},
publisher={Hugging Face},
url={https://huggingface.co/datasets/HAMMALE/rvl_cdip_OCR}
}
🤝 Contribution et Support
- 💬 Discussions: Hugging Face Discussions
- 📧 Contact: Via profil Hugging Face
📄 License
Ce dataset est publié sous licence publique pour encourager la recherche et l'innovation en document understanding.
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