A Study on Various Character Segmentation Techniques on Handwritten Text Documents: A Review

Abstract

Handwritten character segmentation remains one of the most challenging and essential phases in Optical Character Recognition (OCR) and handwritten document analysis. The complexity of unconstrained handwriting, varying writing styles, touching and overlapping characters, inconsistent spacing, and noise significantly affect accurate segmentation and recognition. Traditional segmentation approaches operate primarily on uncompressed images; however, recent studies demonstrate that performing segmentation directly on run-length encoded (RLE) compressed handwritten documents enhances computational efficiency and reduces memory usage. This paper presents a consolidated review and analysis of segmentation methodologies, ranging from explicit segmentation, implicit segmentation, projection-based analysis, connected component analysis, graph-based techniques, clustering approaches, and hybrid recognition-based methods. Furthermore, segmentation strategies for applications including postal address recognition, content-based image retrieval, number plate detection, and cursive word recognition are examined. Experimental findings indicate that hybrid methods, such as min-cut graph methods, dynamic programming, and Hidden Markov Models (HMM), outperform purely classical dissection approaches, particularly for cursive scripts. The study highlights future scope toward deep learning-based models and integrated compressed-domain OCR systems for achieving higher segmentation and recognition accuracy. Overall, this work aims to provide researchers and practitioners with a comprehensive understanding of segmentation challenges, techniques, and advancement trends essential for robust handwritten OCR performance.