Omkar2118/datasets huggingface dataset:
Urdu Text Scene Images

Description:

<a href="https://gts.ai/dataset-download/urdu-text-scene-images/" target="_blank">👉 Download the dataset here</a>

Urdu text extraction in natural scenes poses unique challenges, largely due to the lack of publicly available datasets. To address this, we offer an enriched dataset containing 500 high-quality images of Urdu text captured in real-world environments. These images represent diverse settings, lighting conditions, and backgrounds, making it ideal for researchers and developers working on Urdu Optical Character Recognition (OCR) systems.


Dataset Structure

The dataset is structured as follows:

Training Set (Training Raw): Contains raw images featuring Urdu text for model training.

Test Set (Test Raw): A separate set of images for model testing and validation.

Non-Text Set (Non-Text Raw): Scene images with no Urdu text to prevent false positives and enhance text classification models.

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Applications

This dataset is specifically designed to support the following applications:

Urdu Text Detection & Recognition: Building and fine-tuning OCR models for Urdu script in natural scenes.

Multilingual OCR Systems: Extending existing text recognition systems to include Urdu, especially for South Asian languages with similar script structures.

Autonomous Driving & Navigation Systems: Recognizing Urdu text in street signs, direction boards, and public places, improving functionality in Urdu-speaking regions.

Augmented Reality (AR) Applications: Real-time Urdu text translation or interpretation in natural scenes for tourists or native speakers.

Potential Use Cases

Multilingual Document Digitization: This dataset can be integrated into systems designed for multilingual digitization, where recognizing Urdu text in complex backgrounds is critical.

Urban Planning & Smart Cities: The dataset can aid in the development of systems that recognize text in public areas for smart city initiatives and urban planning efforts.

Mobile Applications: Can be used to enhance mobile apps that need to extract and recognize Urdu text for translation or user interaction.

Future Enhancements

Further dataset releases could include a broader array of text instances, incorporating more variations in fonts, languages (including mixed-language scenarios), and additional annotations like bounding boxes for character-level recognition. This would extend its utility in fields like document analysis, smart OCR solutions, and advanced multilingual systems.


This dataset is sourced from Kaggle.
QR Code Detection YOLO

Description:

<a href="https://gts.ai/dataset-download/qr-code-detection-yolo/" target="_blank">👉 Download the dataset here</a>

This dataset provides a comprehensive platform for enhancing QR code detection models, leveraging both synthetic and real-world data.

Dataset Components

Training Set: Includes thousands of auto-generated QR codes. These QR codes are isolated with no background, allowing the model to focus purely on shape and structure recognition.

Test Set: Consists of real-life images featuring QR codes in varied contexts, from different angles, lighting conditions, and cluttered environments.

Background Images: A curated set of images taken from malls, retail stores, streets, etc., allowing developers to blend QR codes into realistic scenarios, simulating real-world conditions for model training.

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Data Usage and Goals

The primary objective is to train object detection models, such as YOLO (You Only Look Once), using synthetic QR codes and background images to improve generalization on unseen, real-world data. Training on the synthetic set avoids overfitting, while the test set evaluates the model’s performance in natural environments with QR codes placed in cluttered, busy scenes.

Data Augmentation and Variety

To make the dataset more robust, various augmentations are included:

Diverse QR Code Sizes: Codes of varying sizes are use, enabling the model to detect small and large codes in the same setting.

Rotations and Distortions: QR codes are display from multiple angles and subjected to distortions, mimicking real-life conditions where codes may not be perfectly align.

Occlusion Handling: Some QR codes are partially obscured by objects, providing additional complexity for the detection task.

Potential Applications

This dataset is suit for AI developers working on:

Mobile scanning apps

Inventory management systems

Augmented reality applications that rely on QR codes for object recognition.

This dataset is sourced from Kaggle.
Underwater Object Detection Dataset

Description:

<a href="https://gts.ai/dataset-download/underwater-object-detection-dataset/" target="_blank">👉 Download the dataset here</a>

This dataset is designed for advanced underwater object detection and classification. It provides a comprehensive collection of images featuring underwater objects, each precisely annotated with bounding boxes. The dataset aims to support a wide range of research applications, from environmental monitoring to underwater robotics.

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Classes:

Fish (individual and grouped)

Crab

Human Diver

Trash (marine pollution)

Jellyfish

Coral Reef

Sea Turtle

Starfish

Dataset Structure:

Training Set (70%): A robust sample for building detection models.

Validation Set (10%): Used to fine-tune model performance.

Test Set (20%): A carefully selected set of images for evaluating model accuracy.

Pre-processing Techniques:

Auto-Orientation: Ensures all images are correctly aligned.

Resizing: Images are scaled to 640×640 pixels for uniformity.

Brightness Normalization: Corrects for underwater lighting conditions.

Contrast Stretching: Enhances visibility for objects in murky or low-contrast scenes.

New Annotation Techniques:

Polygonal Segmentation: Introduces more precise segmentation for irregular shapes such as coral reefs.

3D Depth Mapping: For enhanced understanding of object placement in underwater space.

Dataset Use Cases:

Marine Ecology: Assessing species diversity and tracking the impact of environmental changes.

Pollution Analysis: Detecting and classifying marine trash, aiding in cleanup efforts.

Underwater Robotics: Training AUVs to recognize and navigate around complex underwater structures like coral reefs or large groups of fish.

Conclusion:

The expanded Underwater Object Detection provides a rich resource for researchers, environmentalists, and engineers working on underwater object detection and classification. Its enhanced classes, precise annotations, and preprocessing techniques make it a valuable asset for developing robust models in marine exploration and conservation.


This dataset is sourced from Kaggle.
RECAPTCHA Dataset for MBBank

Description:

<a href="https://gts.ai/dataset-download/recaptcha-dataset-for-mbbank/" target="_blank">👉 Download the dataset here</a>

In the ever-evolving landscape of digital banking, security is paramount. MB Bank employs reCAPTCHA technology to protect its users from automated bots, ensuring the integrity of online banking systems. This dataset is a comprehensive collection of CAPTCHA images specifically used by MB Bank, designed to bolster research into CAPTCHA-solving techniques.


Dataset Overview


The dataset consists of various CAPTCHA formats used in real-world scenarios. It includes traditional text-based CAPTCHAs, image-based puzzles, and more complex variants that challenge machine learning models while remaining user-friendly.


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Importance for Developers and Researchers
For developers, the dataset provides an opportunity to train models on CAPTCHA recognition, improving system robustness against bot-driven attacks. It helps in building more accurate, adaptive algorithms that can distinguish between human users and bots. Researchers can also use this dataset to explore new CAPTCHA-solving techniques, increasing the success rate of CAPTCHA bypassing in real-time applications.


Applications in Security


The dataset can be used to analyze trends in CAPTCHA security breaches and to develop new methods to safeguard digital platforms. By training on this dataset, developers can create more secure, user-centric CAPTCHA systems that deter fraud while ensuring smooth user experiences.


CAPTCHA Variability and Adaptability

This dataset is ideal for testing algorithms against a wide range of CAPTCHA formats. The variability ensures that AI models are versatile enough to handle different challenges. 

Moreover, the dataset’s rich annotations provide the necessary information to enhance accuracy in CAPTCHA recognition.


Future Potential


The MB Bank reCAPTCHA dataset is not just a valuable resource for improving current security systems but also paves the way for future innovations in online security. As bots become more sophisticated, the need for adaptive and robust CAPTCHA systems grows, making this dataset a critical tool for continuous improvement.


This dataset is sourced from Kaggle.