<![CDATA[Abstract – Line segmentation in handwritten Arab Jawi scripts poses a significant challenge for automatic character recognition systems due to high variations in writing slant. The primary problem arises when using the standard Horizontal Projection Profile (HPP) method, where text skewness causes an overlap between peaks and valleys in the projection graph, leading to inaccurate or failed line separation. This research aims to develop a line segmentation solution for skewed Arab Jawi text by adapting the HPP method through a vertical stripping technique. The methodology involves dividing the document image into several narrow vertical columns or strips, followed by independent horizontal projection calculations for each strip. Local line separation points from each strip are then sequentially connected to form a dynamic separation path that follows the original inclination angle of the text. Research findings demonstrate that this approach successfully separates skewed text lines perfectly without cutting through characters, while also effectively managing variations in manuscript colour degradation and inconsistent line sizes. In conclusion, the modification of HPP with vertical stripping proves to be effective and computationally efficient as a pre-processing stage for ancient Jawi manuscripts. This method offers a balance between the simplicity of classical algorithms and the robustness required to handle handwriting complexity, making it highly potential for integration into the development of broader Optical Character Recognition systems to support the preservation of historical Southeast Asian manuscripts]]>
