Image Processing

The presented dataset encompasses a diverse collection of road images captured under a multitude of environmental conditions, specifically sourced from Tunisian highways. Comprising textual annotations in two languages, this dataset is tailored to facilitate research and development in the domain of scene understanding, language processing, and bilingual context analysis. The collection includes 2006 word pictures with Latin and Arabic text occurrences that were taken from 3000 road scene images.  The dataset's versatility enables investigations into the robustness of lang

In contemporary digital environments, the development of a high-resolution synthetic Latin character dataset holds paramount significance across various real-world applications within the domains of  computer vision and artificial intelligence. This relevance extends from tasks such as image restoration to the implementation of sophisticated recognition systems.

The accurate classification of landfill waste diversion plays a critical role in efficient waste management practices. Traditional approaches, such as visual inspection, weighing and volume measurement, and manual sorting, have been widely used but suffer from subjectivity, scalability, and labour requirements. In contrast, machine learning approaches, particularly Convolutional Neural Networks (CNN), have emerged as powerful deep learning models for waste detection and classification.

The ITM-HDR-VQA dataset is a video quality assessment dataset for inversely tone-mapped videos. It contain 200 HDR10 videos with their MOS.
We capture videos of 20 typical HDR scenes including daylight scenes containing both sunlit areas and deep shadows and night scenes lit by artificial lights. The contents of these scenes can be roughly divided into two categories, man-made architecture and natural scenery.