Abstract: Text detection in natural images and videos is an important and challenging task in computer vision. The ability to detect and recognize text in images and videos has numerous applications in various fields, such as content-based image retrieval, document analysis, scene text recognition, and augmented reality. In recent years, several techniques and algorithms, including edge-based techniques, feature-based techniques, template matching, and machine learning-based methods, have been developed for text detection. Deep learning-based methods have achieved state-of-the-art results in text detection, but they require a large amount of annotated data and computational resources. This article provides an overview of the various techniques and algorithms used for text detection in natural images and videos, as well as their applications and challenges. Additionally, it highlights some popular programming languages that can be used for text detection tasks. Introduction: Text detect...
Abstract There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs. 1. Introduction WSNs are constituted of small senso...