Restoration and Segmentation of Old Jawi Manuscripts using Variational Image Inpainting and Active Contour Models

Abstract

Old Jawi Manuscripts (OJM) are crucial to historical studies, offering insights into past societies. However, degradation from mishandling and environmental factors can impair their legibility. To preserve OJM, image inpainting and segmentation are essential for restoring corrupted areas and identifying text. Recently, the Gaussian Regularization Segmentation (GRS) model has shown effectiveness in intensity inhomogeneity grayscale image segmentation, though it was not designed for corrupted OJM images. Therefore, this study aimed to reformulate the GRS model to restore and segment text from real corrupted OJM images. The methodology begins with the incorporation of the Mumford-Shah and Bertalmio inpainting models into the GRS model as new fitting terms, resulting in the Modified Gaussian Regularization Segmentation Mumford-Shah (MGRSM) model and the Modified Gaussian Regularization Segmentation Bertalmio (MGRSB) model, respectively. MATLAB was used to implement these models, and their performance was assessed on 30 corrupted OJM samples from Malay Ethnomathematics Research Group, with expert evaluations and efficiency measured by average elapsed time. The MGRSM model achieved 38.4 percent and 12.4 percent higher overall total scores from experts in terms of segmentation accuracy compared to the GRS and MGRSB models, respectively. While the GRS model is the fastest, the MGRSM model provides superior accuracy, with an average processing time of 9.35 seconds, making it the most optimal for restoring and segmenting OJM images. This approach not only enhances the preservation of historical manuscripts but also provides a practical tool for researchers and historians in safeguarding our cultural heritage.