README.md

---
license: cc-by-4.0
pretty_name: Handwriting Recognition Dataset
language:
  - en
tags:
  - handwriting
  - ocr
  - computer-vision
  - text-recognition
  - ai-research
  - handwritten-text
task_categories:
  - image-classification
size_categories:
  - n<1K
---

# Dataset Card for Handwriting Recognition Dataset

This dataset contains a collection of handwritten text images designed to improve OCR (Optical Character Recognition) and text recognition models. Each image is labeled with a transcription of the same sentence, allowing models to learn to map handwritten content to its textual equivalent.

## Dataset Details

### Dataset Description

This dataset contains images of handwritten English text contributed by various individuals. Each image includes the same standard sentence:

> "AI learns from data. Your handwriting helps machines read text better. Write clearly; good handwriting boosts AI accuracy. This small act aids AI research. Thanks for your support!"

The dataset is ideal for training and evaluating OCR models and applications involving handwritten text recognition.

## Uses

### Direct Use

- Training OCR models to recognize English handwritten text.
- Fine-tuning vision models on handwritten content.
- Educational purposes in AI research and ML bootcamps.

### Out-of-Scope Use

- Real-time handwriting verification or personal identity recognition.
- Commercial use without proper attribution under CC BY 4.0.
- Any use that attempts to link handwriting to individuals.

## Dataset Structure

Each sample consists of:

- An image (`.jpg` or `.png`) stored in the `images/` directory.
- A `metadata.csv` file with columns:
  - `file_name`: name of the image file (e.g., `sample_01.jpg`)
  - `text`: transcription of the handwritten sentence (identical for all rows)

## Dataset Creation

### Curation Rationale

The dataset was curated to help improve handwritten text recognition, especially for machine learning systems that require structured, consistent inputs.

### Source Data

#### Data Collection and Processing

Contributors were asked to write a standard sentence on paper and scan or photograph it under good lighting. All images were manually checked for clarity, contrast, and legibility.

#### Who are the source data producers?

Anonymous contributors with diverse handwriting styles. No personal data was collected.

### Annotations

#### Annotation process

Each image is paired with the same predefined sentence. Since all transcriptions are identical, no manual transcription was required.

#### Personal and Sensitive Information

No personally identifiable or sensitive data is included in the dataset.

## Bias, Risks, and Limitations

- Handwriting samples may lack diversity in script style and regional variations.
- All samples use English and the same sentence — not suitable for language modeling or multilingual OCR.
- Models trained on this dataset may not generalize well to varied real-world handwriting.

### Recommendations

- Combine with other handwritten datasets for broader coverage.
- Use only for academic, non-commercial experimentation unless explicitly licensed.

---
## Contact

  - For queries or collaborations related to datasets, contact at :
    - anoushka@kgen.io
    - abhishek.vadapalli@kgen.io

## Citation

**BibTeX:**

```bibtex
@misc{handwriting_recognition_dataset,
  title = {Handwriting Recognition Dataset},
  author = {Various Contributors},
  year = {2025},
  howpublished = {\url{https://huggingface.co/datasets/your-org/handwriting-recognition}},
  note = {Dataset available under CC BY 4.0 license}
}