Unmasking Embedded Text: A Deep Dive into Scene Image Analysis

Text extraction is a crucial step in computer vision and document analysis. It involves finding and extracting text from photos and videos, which can provide valuable information. This is a challenging task due to the variations in text size, fonts, styles, alignments, contrasts, and background. However, many academics have concentrated on text extraction in pictures due to the rise in multimedia documents and the requirement for information retrieval. Numerous techniques, including the Connected Component Method, the Mathematical Morphology Method, the Edged Based Method, the Wavelet Transform, the Texture Features, and the Artificial Neural Network, have been developed by researchers to extract text from images. However, in terms of accuracy, review, and f-score, each approach has certain benefits and limitations. This review paper provides a comprehensive study of various text extraction techniques and discusses future research possibilities. It also summarizes recent research on text extraction from scene images and evaluates the advantages and disadvantages of different approaches. The paper concludes by comparing the performance of various methods for text extraction in scenes and discusses commonly used benchmark datasets and evaluation methods. The overall goal of the article is to provide resources for newcomers to the topic and encourage further exploration of the subject.