Identification of paleographic curvature using skeletonization and key point detection

Abstract

Jawi script represents a vital component of the Islamic intellectual heritage of the Nusantara, preserved across numerous classical manuscripts. A primary challenge in digitizing these documents is character segmentation, particularly where handwritten characters connect without distinct boundaries. This research proposes a skeletonization-based segmentation method to address this issue, utilizing a dataset from 17 pages of the “Kitab Syair Perahu” manuscript containing 269 test characters. The pre-processing stage involves grayscale conversion, binarization, and noise removal through connected component analysis (CCA). The segmentation process then integrates skeleton structures, centroid positioning, intersection points, and loop detection. Evaluation results show the system successfully identified 187 out of 269 characters, achieving an accuracy of 0.801, a precision of 0.895, a recall of 86.38%, and an F1-score of 88.91%. While these results demonstrate the method’s effectiveness, the small dataset from a single manuscript limits its generalizability. Nevertheless, this study establishes a foundational step toward an automated Jawi image-processing system and the digital preservation of Islamic Nusantara literacy, contributing a tailored skeletonization-based approach for Jawi script.