Machine Learning Algorithm for Text Detection

Text detection is a vital component of computer vision and natural language processing (NLP), underpinning diverse applications such as document digitization, real-time fraud prevention, multimedia retrieval, and augmented reality. This study explores the evolution of text detection, from traditional machine learning approaches to modern deep learning paradigms like CNNs, RNNs, and Vision Transformers (ViTs). Focusing on challenges such as multilingual scripts, handwritten text, and natural scenes, it aims to enhance detection accuracy, efficiency, and scalability. Furthermore, the research emphasizes lightweight architectures for deployment in resource-constrained environments, contributing to assistive technologies, real-time monitoring systems, and multimedia content indexing.