Developments in Text Extraction from Images: Comparative Evaluation of Various Techniques

Extracting text from images is essential in image processing and computer vision, with applications in document digitization and automated text recognition. This paper reviews various text extraction techniques, categorized into thresholding, rough set and fuzzy set methods, clustering, edge detection, and machine learning. Thresholding techniques such as Gaussian, Otsu, adaptive, and double-edge methods are explored. Rough set and fuzzy set methods, which handle uncertainty in image data and improve text segmentation, are reviewed. Clustering techniques, such as K-means and density-based methods, are studied for their effectiveness in grouping pixels for text isolation. Edge detection techniques, including Sobel, Roberts, Canny, Morphological Component Analysis (MCA), and Laplacian, are examined for their role in enhancing text boundary identification. Machine learning approaches, such as Support Vector Machines (SVM), Contrastive Language-Image Pre-training (CLIP), Hidden Markov Models (HMM), and hybrid BiLSTM-CNN models, are analyzed for their ability to improve accuracy in noisy environments. This review compares these techniques, highlighting their strengths, challenges, and applications for text extraction.