<![CDATA[The preservation of Balinese lontar manuscripts has become increasingly important due to their cultural, historical, and religious significance, while physical degradation such as uneven illumination, faded ink, texture interference, and manuscript aging continues to reduce readability and complicate digital preservation efforts. This study proposes an end-to-end Optical Character Recognition (OCR) framework for degraded Balinese lontar manuscripts by integrating Bayesian-optimized image enhancement, adaptive preprocessing, morphology-based segmentation, domain-specific augmentation, and lightweight deep learning recognition using MobileNetV3. The proposed enhancement pipeline combines Multiscale Retinex, adaptive gamma correction, edge-preserving filtering, and hybrid binarization to improve character visibility under degraded manuscript conditions. Bayesian Optimization with Optuna and Tree-structured Parzen Estimator (TPE) was employed to automatically optimize enhancement parameters according to manuscript quality characteristics. Experimental results demonstrated substantial improvements in manuscript image quality, where Laplacian Variance increased from 306.7596 to 6685.7641, RMS Contrast improved from 28.976 to 83.9085, Michelson Contrast increased from 0.8238 to 1.0, and Ink Ratio Score improved from 0.6096 to 0.9847. The MobileNetV3-based OCR recognition model achieved a test accuracy of 80.52% and a best validation accuracy of 83.78% across 102 Balinese script classes. The proposed framework demonstrates that adaptive enhancement optimization combined with lightweight OCR recognition can provide robust and computationally efficient recognition performance for degraded historical manuscripts while supporting scalable digital preservation and mobile-oriented cultural heritage applications.]]>
