In the recent days, the research area of handwritten character recognition has got much attention toward ancient inscriptions, since they contain lots of unfolding knowledge in the field of science, literature, astronomy, medicine, etc. The materials used to write these inscriptions are paper, palm leaf, stone rocks, and temple walls, etc., and these materials are now degrading in nature due to climatic conditions, ink bleeding, lack of attention, and unscientific storage. In this paper, the digitization and restoration of Kannada handwritten palm leaf with iterative global threshold based segmentation is developed. The performance estimation is measured by calculating MSE and PSNR values, and the image quality is compared to manual obtained results by epigraphists. The average values of PSNR and MSE are 6.198 and 0.234, respectively. The higher the PSNR and lower the MSE determines the quality of the image. The outcomes are also compared to other standard methods, namely, Souvola, Niblack, and Adaptive thresholding (Gaussion+Binary Inverse). The comparison studies confirm that the proposed algorithm is more effective than the other methods. The proposed algorithm is also implemented on the benchmark standard palm leaf dataset, i.e., the AMADI_LONTARSET.