Omkar2118/datasets huggingface dataset:
Forchheim Image Dataset

Description:

<a href="https://gts.ai/dataset-download/forchheim-image-dataset/" target="_blank">👉 Download the dataset here</a>

The Forchheim Image Dataset is design specifically for Source Camera Identification (SCI) tasks, offering a diverse range of images captured from various devices. This dataset is an essential resource for forensic and cybersecurity professionals who are working to trace the origin of digital images to the cameras that captured them.

The dataset contains a total of 3,851 high-resolution images, meticulously curated from 27 distinct digital devices, ensuring broad representation across different camera models and manufacturers. To maintain the focus on Source Camera Identification, only the ‘original’ (unprocessed) images from each device have been retain, while all other derivative files, such as edited or compressed versions, have been exclude.

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Key Features of the Forchheim Dataset:

Diversity of Devices: Includes images from 27 unique devices, ranging from smartphones to high-end cameras, covering various sensor types, lenses, and software configurations.

High-Quality Images: All images are preserved in their original, unaltered formats to ensure authenticity and integrity for SCI tasks.

Exclusively for SCI: Derivative files and any post-processed images have been remove, ensuring that the dataset strictly serves the purpose of source camera identification.

Applications: Ideal for forensic analysis, digital media forensics, image authentication, and cybersecurity research where tracing the origin of images is critical.

Dataset Structure: The dataset is organize into folders by device, making it easier for researchers to access and analyze images base on their source.

Potential Use Cases:

Forensic Analysis: Identifying the source of images in legal cases or criminal investigations.

Cybersecurity: Detecting manipulated or unauthorized images used in malicious campaigns.

Academic Research: Training and testing machine learning models for image source attribution

This dataset is sourced from Kaggle.
Urdu Handwritten Text Dataset

Description:

<a href="https://gts.ai/dataset-download/urdu-handwritten-text-dataset/" target="_blank">👉 Download the dataset here</a>

This dataset consists of high-quality images of handwritten text in the Urdu language, one of the most commonly spoken languages in South Asia, especially in Pakistan, India, and surrounding regions. The dataset has been created by inviting native Urdu speakers from diverse social, educational, and cultural backgrounds to write a predefined text in their natural handwriting style. This predefined text was carefully curated to cover the full range of Urdu characters, ligatures, diacritics, dots, and special symbols used in everyday writing.

Dataset Features

Diverse Handwriting Styles: The dataset includes contributions from native speakers across different demographics, ensuring a rich variety of handwriting styles.

Comprehensive Character Set: The predefined text covers all characters, ligatures, diacritics, and dots commonly used in Urdu script.

Inclusivity: Contributions from people with disabilities add unique variations to the dataset, making it more diverse and comprehensive.

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Demographic Information

The demographic details of contributors, including age, gender, and educational background, are recorded. This information is particularly valuable for research related to author identification, handwriting analysis, and text-matching algorithms.

Potential Applications

This dataset has numerous applications, including:

OCR Development: Enhancing Optical Character Recognition systems for Urdu text.

Handwriting Authentication: Improving security through handwriting-based user verification.

Linguistic Studies: Supporting research in Urdu language processing, script digitalization, and handwriting analysis.

Forensic Handwriting Analysis: Assisting in forensic research for identifying individual handwriting patterns.

Multilingual Handwriting Recognition: Building robust AI models that can recognize handwriting across different languages and scripts.

Quality Control

The dataset has undergone a rigorous quality check to ensure consistency, accuracy, and usability across various academic and commercial research projects, particularly those that involve natural language processing and computer vision technologies.

This dataset is sourced from Kaggle.