Handwritten text recognition is a crucial task in document digitization, requiring accurate preprocessing and segmentation for effective Optical Character Recognition (OCR). This study focuses on developing an efficient preprocessing pipeline to enhance handwritten text quality and improve word segmentation accuracy using the IAM dataset. The proposed rule-based approach integrates standard image processing steps—grayscale conversion, Otsu’s adaptive thresholding, and morphological operations in a novel sequential framework that strengthens word separation and suppresses background noise. A contour-based segmentation technique is then applied to accurately detect and isolate words. Experimental results demonstrate that the preprocessing pipeline significantly improves text clarity and segmentation accuracy, making it suitable for further OCR applications. The average result of segmentation achieved is 96.15% on English handwritten text, excluding the cursive style of handwriting. This work contributes to the advancement of handwritten text recognition by providing a robust preprocessing framework for complex handwritten documents. A comparative analysis highlights the trade-offs between traditional projection-based methods, deep learning approaches, and the proposed morphological segmentation. This study contributes to the field by offering an optimized preprocessing pipeline that improves word segmentation accuracy, ultimately aiding in better recognition performance for handwriting analysis systems. The proposed method provides a practical solution for automated handwritten document processing, making it valuable for applications such as digital archiving, transcription, and OCR preprocessing.