DastKhat

Persian Handwritten Text Dataset

DastKhat (دستخط) is a Persian handwritten text dataset created for research and development in Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR).

The dataset contains handwritten Persian sentences collected from multiple participants. Each participant wrote a collection of predefined Persian sentences, producing handwritten samples with different writing styles and characteristics.

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Dataset Links

GitHub Repository

The GitHub repository contains the project's documentation, metadata, and data processing tools:

https://github.com/AmirRezaFarahmand/DastKhat

Hugging Face Dataset

This repository contains the actual handwritten image data and its associated metadata.

also checkout our website for more information on contributing to the project.

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Dataset Contents

The dataset consists of handwritten Persian sentence images and metadata describing each sample.

Each sample contains:

Field	Description
sample_id	Unique identifier for the handwritten sample
participant_id	Identifier of the participant who wrote the sample
image	The handwritten image
sentence_id	Identifier of the sentence
text	Ground-truth Persian transcription
split	Dataset split, such as train, validation, or test

A typical sample can be represented as:

{
    "sample_id": "000001",
    "participant_id": "participant_001",
    "image": <image>,
    "sentence_id": "000001",
    "text": "متن فارسی دست‌نویس",
    "split": "train"
}

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Dataset Structure

The dataset is organized around handwritten sentence images.

Each image is associated with:

• A unique sample identifier
• The participant who wrote the sample
• The sentence identifier
• The original Persian text
• A dataset split

The dataset is designed to preserve writer diversity, allowing models and experiments to work with handwriting from different individuals.

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Data Collection

The data was collected from multiple participants.

Participants were provided with predefined Persian sentences and asked to write them by hand. The handwritten submissions were then processed into individual image samples.

The general data preparation process included:

1. Collecting handwritten Persian samples
2. Extracting handwritten regions from submitted documents
3. Cropping individual sentence images
4. Associating each image with its corresponding sentence
5. Assigning participant identifiers
6. Preparing metadata
7. Organizing the final dataset

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Why Writer Diversity Matters

Persian handwriting varies significantly between individuals.

Differences can occur in:

• Letter shapes
• Character connections
• Word spacing
• Writing size
• Slant
• Stroke structure
• Pen pressure
• Overall writing style

Because of this, datasets containing handwriting from multiple writers are important for developing recognition systems that generalize beyond a single person's handwriting.

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Intended Uses

DastKhat can be used for:

• Persian Handwritten Text Recognition (HTR)
• Persian Optical Character Recognition (OCR)
• Computer Vision
• Deep Learning
• Document Image Analysis
• Writer Identification
• Handwriting Style Analysis
• Data Augmentation Research
• Sequence Recognition

The dataset is particularly suitable for experiments involving image-to-text recognition of handwritten Persian sentences.

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Loading the Dataset

You can load the dataset using the Hugging Face datasets library:

python -m pip install datasets

from datasets import load_dataset

dataset = load_dataset("MrFarahmand/DastKhat")

Inspect the dataset:

print(dataset)

Access a sample:

sample = dataset["train"][0]

print(sample)

the image can be accessed from the image field:

image = sample["image"]
text = sample["text"]

print(text)
image.show()

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Dataset Splits

The dataset includes split information associated with the samples.

The available splits may include:

• train
• validation
• test

The exact number of samples in each split may change as the dataset is expanded.

For the most up-to-date dataset statistics, please refer to the dataset viewer and dataset metadata on this page.

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Limitations

DastKhat is an actively developing dataset.

Potential limitations include:

• The number of participants may be limited compared to large-scale handwriting datasets.
• The dataset currently focuses on handwritten Persian sentences.
• Handwriting styles may not represent the full diversity of Persian handwriting.
• Dataset size and composition may change as new samples are collected.

Users should consider these limitations when evaluating model performance.

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Dataset Status

🚧 Active Development

The dataset may be expanded with:

• Additional participants
• More handwritten sentences
• More writing styles
• Improved metadata
• Additional dataset splits
• Improved quality control

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License

Please refer to the license specified in the dataset repository for information about permitted use, modification, and redistribution.

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Citation

If you use DastKhat in your research or project, please cite this dataset.

A formal citation will be added as the dataset reaches a stable release.

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Acknowledgements

Special thanks to all participants who contributed their handwritten Persian samples.

Their contributions made the creation of the DastKhat dataset possible.

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Maintainer

Amir Reza Farahmand

• GitHub: https://github.com/AmirRezaFarahmand
• Hugging Face: https://huggingface.co/MrFarahmand