README.md

---
license: apache-2.0
task_categories:
- image-to-text
- image-classification
language:
- en
size_categories:
- 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