T Techniques of Text Detection and Recognition: A Survey

INTRODUCTION

Abstract

This survey paper aims to provide an overview of the various techniques used for text detection and recognition in images and videos. Text detection and recognition are important tasks in the field of computer vision and have numerous applications such as document analysis, scene understanding, and video indexing. In this paper, we will review the different approaches and algorithms that have been proposed for text detection and recognition, including traditional methods based on image processing techniques as well as more recent deep learning-based approaches. We will also discuss the challenges and future directions in this field. One common approach to text detection in images is to use edge detection algorithms to locate regions of high contrast that may contain text. Once potential text regions have been identified, various techniques, such as connected component analysis or stroke width transform, can be used to extract individual characters or words. For text recognition, optical character recognition (OCR) algorithms are often employed to convert the extracted text into machine-readable format. Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown promising results in text detection and recognition tasks, outperforming traditional methods in many cases. Despite the progress that has been made in this field, challenges such as handling complex backgrounds, varying fonts and sizes, and low-quality images still remain. Future research directions may focus on developing more robust and accurate algorithms that can handle these challenges effectively.