Omkar2118/datasets huggingface dataset:
Media-Text
Full name: MediaText: a media industry-based dataset for scene text detetcion

**Media-Text** dataset comprising images of banners, posters, covers and another images characterised for media industry.

###  **DATASET DESCRIPTION**
- 400 images
- 7 744 annotated text instances
- 973 annotations have been marked as illegible for the task of text recognition
- 659 texts have been markes as do not care (###) for scene text detection.
- Images are represented by 193 unique resolutions.
Annotation Format - Each image has corresponding  gt_*.txt file, which contains annotations in bounding box format (defined by 4 courners), transcription, and bool flag which determines that text is illegible for OCR. Proposed format is similar to ICDAR15 annotations.

x1, x2, ..., x4, y4, transcription, OCR Flag 

**Example: **

37,68,198,49,214,181,52,200,LADIES,False

**Full paper: **  [ResearchGate](https://www.researchgate.net/publication/385351709_Media-Text_a_Media_Industry-Based_Dataset_for_Scene_Text_Detection)


Please cite the related works in your publications if it helps your research:
<br>
*S. Kalisz, M. Marczyk, J. Polańska, and R. Fagas, “Media-text: a media industry-based dataset for scene text detection,” in Modelling and simulation 2024. The 2024 European Simulation and Modelling Conference, M. Graña and J. D. Nuñez-Gonzalez, Eds., EUROSIS-ETI, 2024, pp. 138–144.*
Google Ranked URLs Dataset

This dataset was curated for Search Engine Optimization (SEO) analysis tasks, including categorization and spam detection. It covers 12 diverse topics: basketball, books, cats, gardening, history, movies, music, recipes, sports, technology, travel, and weather. Some topics have hierarchical relationships, such as sports and basketball, while others are closely related (e.g., movies and music) or unrelated (e.g., basketball and gardening), with varying degrees of overlap among them. For each topic, approximately 300 search queries were generated using large language models (LLMs) like GPT, Llama, and Claude. The top 10 URLs from the Google Search Console’s search engine results page (SERP) were retrieved for each query.

Introducing paper: QuickCharNet: An Efficient URL Classification Framework for Enhanced Search Engine Optimization