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Palm leaf manuscripts (PLMs) are of great importance in recording Buddhist Scriptures, medicine, history, philosophy, etc. Some damages occur during the use, spread, and preservation procedure. The comprehensive investigation of Sanskrit PLMs is a prerequisite for further conservation and restoration. However, current damage identification and investigation are carried out manually. They require strong professional skills and are extraordinarily time-consuming. In this study, PLM-SegFormer is developed to provide an automated damage segmentation for Sanskrit PLMs based on the SegFormer architecture. Firstly, a digital image dataset of Sanskrit PLMs (the PLM dataset) was obtained from the Potala Palace in Tibet. Then, the hyperparameters for pre-processing, model training, prediction, and post-processing phases were fully optimized to make the SegFormer model more suitable for the PLM damage segmentation task. The optimized segmentation model reaches 70.1% mHit and 51.2% mIoU. The proposed framework automates the damage segmentation of 10,064 folios of PLMs within 12 h. The PLM-SegFormer framework will facilitate the preservation state survey and record of the Palm-leaf manuscript and be of great value to the subsequent preservation and restoration. The source code is available at https://github.com/Ryan21wy/PLM_SegFormer. [ABSTRACT FROM AUTHOR]