Recognizing texts in video is more complex than in other environments such as scanned documents. Video texts appear in various colors, unknown fonts and sizes, often affected by compression artifacts and low quality. In contrast to Latin texts, there are no publicly available datasets which cover all aspects of the Arabic Video OCR domain. This paper describes a new well-defined and annotated Arabic-Text-in-Video dataset called AcTiV 2.0. The dataset is dedicated especially to building and evaluating Arabic video text detection and recognition systems. AcTiV 2.0 contains 189 video clips serving as a raw material for creating 4063 key frames for the detection task and 10,415 cropped text images for the recognition task. AcTiV 2.0 is also distributed with its annotation and evaluation tools that are made open-source for standardization and validation purposes. This paper also reports on the evaluation of several systems tested under the proposed detection and recognition protocols.

In the era of mobile imaging, the quality of document photos captured by smartphones often suffers due to adverse lighting conditions. Traditional document analysis and optical character recognition systems encounter difficulties with images that have not been effectively binarized, particularly under challenging lighting scenarios. This paper introduces a novel adaptive binarization algorithm optimized for such difficult lighting environments. Unlike many existing methods that rely on complex machine learning models, our approach is streamlined and machine-learning free, designed around integral images to significantly reduce computational and coding complexities. This approach enhances processing speed and improves accuracy without the need for computationally expensive training procedures. Comprehensive testing across various datasets, from smartphone-captured documents to historical manuscripts, validates its effectiveness. Moreover, the introduction of versatile output modes, including color foreground extraction, substantially enhances document quality and readability by effectively eliminating unwanted background artifacts. These enhancements are valuable in mobile document image processing across industries that prioritize efficient and accurate document management, spanning sectors such as banking, insurance, education, and archival management.

Synthetic time series generation is an emerging field of study in the broad spectrum of data science, addressing critical needs in diverse fields such as finance, meteorology, and healthcare. In recent years, diffusion methods have shown impressive results for image synthesis thanks to models such as Stable Diffusion and DALL·E, defining the new state-of-the-art methods. In time series generation, their potential exists but remains largely unexplored. In this work, we demonstrate the applicability and suitability of diffusion methods for time series generation on several datasets with a rigorous evaluation procedure. Our proposal, inspired from an existing diffusion model, obtained a better performance than a reference model based on generative adversarial networks (GANs). We also propose a modification of the model to allow for guiding the generation with respect to conditioning variables. This conditioned generation is successfully demonstrated on meteorological data.