Update README.md

README.md

@@ -1,7 +1,317 @@
 ---
 license: apache-2.0
+task_categories:
+- image-to-text
+- image-classification
 language:
 - en
 size_categories:
-- 100B<n<1T
----
+- 1M<n<10M
+tags:
+- handwriting-recognition
+- htr
+- self-supervised-learning
+- historical-documents
+- writer-identification
+pretty_name: SSL-HWD
+---
+
+# SSL-HWD (A Large Scale Handwritten Image Dataset)
+
+## Dataset Description
+
+### Dataset Summary
+
+**SSL-HWD** is a large-scale handwritten text dataset introduced in the paper ["Learning Beyond Labels: Self-Supervised Handwritten Text Recognition"](https://logo-ssl.github.io/) (WACV 2026). The dataset comprises **10 million word-level handwritten images** from **852 writers** across diverse domains including Physics, Computer Science, Biology, Mathematics, and more.
+
+The dataset is specifically designed to support **self-supervised learning** approaches for Handwritten Text Recognition (HTR), addressing the critical challenge of reducing dependence on large volumes of labeled data.
+
+### Dataset Composition
+
+- **Total Words**: 10 million word-level images
+- **Writers**: 852 unique contributors
+- **Pages**: 81,280 scanned document pages
+- **Domains**: 20+ domains including sciences, literature, and mathematics
+- **Labeled Subset**: 2.08M words (20.8%) with ground truth transcriptions
+- **Unlabeled Subset**: 7.92M words (79.2%) for self-supervised pretraining
+- **Unique Vocabulary**: 107,813 unique words
+
+### Key Features
+
+**Diversity and Complexity**: The dataset includes challenging real-world scenarios:
+- Texts with different font colors (varying ink and pen usage)
+- Texts with difficult backgrounds (lines, noise interference)
+- Texts with distorted characters (irregular strokes, structural inconsistencies)
+- Texts with blurring effects (motion or focus issues)
+- Texts with highlighted backgrounds (color markings obscuring content)
+
+**Quality Assurance**:
+- All samples automatically annotated using Amazon Textract with confidence ≥99%
+- Labeled subset manually verified by language experts
+- High-quality word segmentation with precise bounding boxes
+
+### Comparison with Existing Datasets
+
+| Dataset | Pages | Writers | Words | Unique Words |
+|---------|-------|---------|-------|--------------|
+| IAM | 1.5K | 657 | 115K | 10.5K |
+| GNHK | 687 | - | 39K | 12.3K |
+| IIIT-HW-English-Word | 20.8K | 1,215 | 757K | 174K |
+| **SSL-HWD (Ours)** | **81.2K** | **852** | **10M** | **107K** |
+
+### Vocabulary Distribution
+
+| Category | SSL-HWD |
+|----------|---------|
+| Alphabetic Words | 61,088 |
+| Numeric Words | 4,981 |
+| Stop-words | 457 |
+| Other Words | 41,287 |
+| **Total Unique** | **107,813** |
+
+## Dataset Structure
+
+### Data Organization
+
+```
+SSL-HWD/
+├── labeled/              # 2.08M labeled samples
+│   ├── writer1/
+│   │   ├── writer1.csv   # Ground truth transcriptions
+│   │   ├── writer1_1.png
+│   │   ├── writer1_2.png
+│   │   └── ...
+│   ├── writer2/
+│   └── ...
+└── unlabeled/            # 7.92M unlabeled samples
+    ├── writer1/
+    │   ├── writer1_1.png
+    │   ├── writer1_2.png
+    │   └── ...
+    └── ...
+```
+
+### Data Fields
+
+**CSV Format (for labeled data)**:
+- `image_filename` (string): Name of the word image file
+- `transcription` (string): Ground truth text transcription
+
+**Example**:
+```csv
+image_filename,transcription
+writer1_1.png,handwritten
+writer1_2.png,recognition
+writer1_3.png,dataset
+```
+
+### Data Splits
+
+The labeled subset (2.08M samples) is divided as follows:
+- **Training**: 60% (1.25M samples)
+- **Testing**: 40% (0.83M samples)
+
+The unlabeled subset (7.92M samples) is used for self-supervised pretraining.
+
+## Supported Tasks
+
+### 1. Handwriting Text Recognition (HTR)
+Train models to recognize handwritten text from word images.
+
+### 2. Self-Supervised Learning
+Use the large unlabeled subset for pretraining with methods like contrastive learning.
+
+### 3. Writer Identification
+Identify writers based on handwriting characteristics (852 unique writers).
+
+### 4. Domain Adaptation
+Transfer learning across different handwriting styles and domains.
+
+### 5. Semi-Supervised Learning
+Combine small labeled and large unlabeled subsets for improved performance.
+
+## Usage
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load the full dataset
+dataset = load_dataset("your-username/ssl-hwd")
+
+# Access labeled data
+labeled = dataset['labeled']
+
+# Access unlabeled data for self-supervised learning
+unlabeled = dataset['unlabeled']
+```
+
+### Basic Example
+
+```python
+from PIL import Image
+import pandas as pd
+from pathlib import Path
+
+# Load a writer's data
+writer_folder = Path("labeled/writer1")
+df = pd.read_csv(writer_folder / "writer1.csv")
+
+# Load first image and transcription
+img_name = df.iloc[0]['image_filename']
+transcription = df.iloc[0]['transcription']
+
+image = Image.open(writer_folder / img_name)
+print(f"Transcription: {transcription}")
+```
+
+### PyTorch DataLoader
+
+```python
+import torch
+from torch.utils.data import Dataset, DataLoader
+from PIL import Image
+import pandas as pd
+
+class SSLHWDDataset(Dataset):
+    def __init__(self, writer_folder, transform=None):
+        self.folder = Path(writer_folder)
+        csv_file = list(self.folder.glob("*.csv"))[0]
+        self.data = pd.read_csv(csv_file)
+        self.transform = transform
+        
+    def __len__(self):
+        return len(self.data)
+    
+    def __getitem__(self, idx):
+        img_name = self.data.iloc[idx]['image_filename']
+        transcription = self.data.iloc[idx]['transcription']
+        
+        img_path = self.folder / img_name
+        image = Image.open(img_path).convert('RGB')
+        
+        if self.transform:
+            image = self.transform(image)
+            
+        return image, transcription
+
+# Usage
+dataset = SSLHWDDataset('labeled/writer1')
+dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
+```
+
+## Benchmark Results
+
+### State-of-the-Art Performance (from LoGo-HTR paper)
+
+**IAM Dataset**:
+- With 80% labeled data: WER 11.93%, CER 2.31%
+- With 100% labeled data: WER 10.27%, CER 2.01%
+
+**GNHK Dataset**:
+- With 80% labeled data: WER 19.69%, CER 9.05%
+- With 100% labeled data: WER 12.07%, CER 7.20%
+
+**RIMES Dataset**:
+- With 80% labeled data: WER 6.15%, CER 1.89%
+- With 100% labeled data: WER 5.50%, CER 1.78%
+
+**LAM Dataset** (line-level):
+- With 80% labeled data: WER 7.2%, CER 3.2%
+- With 100% labeled data: WER 6.3%, CER 2.39%
+
+### Cross-Dataset Generalization
+
+The dataset demonstrates strong cross-dataset transfer capabilities:
+- SSL-HWD → IAM: WER 13.2%, CER 2.9%
+- SSL-HWD → GNHK: WER 10.1%, CER 6.8%
+- SSL-HWD → RIMES: WER 11.2%, CER 3.5%
+- SSL-HWD → LAM: WER 16.4%, CER 7.2%
+
+## Dataset Creation
+
+### Source Data
+
+The dataset was curated from publicly available digitized manuscripts from web sources, selected for being fully or substantially handwritten. Documents span:
+- Personal diaries
+- Academic notes
+- Historical correspondence
+- Scientific manuscripts
+- Mathematical writings
+- Literature and more
+
+### Data Quality
+
+- **Diverse Sources**: 852 unique writers across 20+ domains
+- **Real-world Challenges**: Includes blur, noise, distortions, and background interference
+
+## Applications
+
+### Self-Supervised Learning (Primary Use)
+Use the 7.92M unlabeled samples for pretraining with methods like:
+- Contrastive learning (SimCLR, MoCo)
+- Masked image modeling
+- Local-global objectives (as in LoGo-HTR)
+
+### Semi-Supervised Learning
+Combine labeled and unlabeled subsets for improved performance with limited annotations.
+
+### Few-Shot Learning
+Train models with minimal labeled data by leveraging pretrained representations.
+
+### Transfer Learning
+Pretrain on SSL-HWD and fine-tune on domain-specific datasets.
+
+## Limitations and Considerations
+
+### Known Limitations
+
+- **Language**: Primarily English handwritten text
+- **Geographic Bias**: Predominantly Western handwriting styles
+- **Historical Period**: Concentrated in specific time periods
+- **Domain Coverage**: While diverse, may not represent all handwriting variations
+
+### Ethical Considerations
+
+- Dataset contains historical documents and handwritten materials
+- Personal information may be present in some samples
+- Users should be aware of privacy considerations when using this data
+
+## Citation
+
+If you use the SSL-HWD dataset in your research, please cite:
+
+```bibtex
+@inproceedings{mitra2026learning,
+  title={Learning Beyond Labels: Self-Supervised Handwritten Text Recognition},
+  author={Mitra, Shree and Mondal, Ajoy and Jawahar, C. V.},
+  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
+  year={2026}
+}
+```
+
+## Additional Resources
+
+- **Project Website**: [https://logo-ssl.github.io/](https://logo-ssl.github.io/)
+
+## License
+
+This dataset is released under the **Apache License 2.0**.
+
+## Acknowledgments
+
+This work is supported by MeitY, Government of India, through the NLTM-Bhashini project.
+
+## Contact
+
+For questions or issues regarding the dataset:
+- **Authors**: Shree Mitra, Ajoy Mondal, C.V. Jawahar
+- **Institution**: IIIT Hyderabad
+- **Email**: shree.mitra@research.iiit.ac.in
+
+---
+
+**Dataset Version**: 1.0  
+**Last Updated**: January 2026  
+**Status**: Active