Image enhancement and noise removal are critical preprocessing steps in computer vision and image analysis. This paper presents a Hybrid Image Enhancement and Noise Removal Algorithm that integrates classical image processing techniques with statistical clustering methods to achieve robust denoising while preserving essential image details. The proposed approach begins by converting the RGB input image to grayscale, followed by adaptive binarization using Otsu’s method to improve contrast. Morphological erosion is applied to eliminate small-scale noise, while gradient-based analysis detects seed points for relevant image regions. A flood-fill algorithm is employed to identify connected components, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is used to separate noise from meaningful structures. Finally, the image undergoes enhancement in the HSV color space, adjusting pixel values based on statistical measures to refine the output. Unlike existing methods, this approach integrates gradient-based seed detection with DBSCAN clustering to effectively distinguish text from noise in historical degraded documents. Unlike existing methods that rely on OCR-based validation, our approach evaluates readability using a structured Likert-scale assessment conducted by expert reviewers. Experimental results demonstrate a significant readability improvement, with average scores increasing from 1.8 (pre-enhancement) to 4.5 (post-enhancement). These findings establish the proposed method as a robust solution for restoring degraded MODI script documents, addressing this script’s lack of automated OCR systems.