Texture Detection for Letter Carving Segmentation of Ancient Copper Inscriptions

Abstract

As relics of history, ancient copper inscriptions are found in many countries. Information in the image or letter forms contained on copper ancient inscription has a very high value. The age and environmental factors caused damage to the surface of the inscription and also reduced the appearances of the image and letter. In this paper, we describe a novel segmentation methodology based on multi-texture features for ancient copper inscriptions which were severely damaged. The segmentation results of letters on ancient copper inscriptions by using the proposed method have an average accuracy of 90%. Based on these results, the proposed method is suitable for letter segmentation of the ancient copper inscriptions.

Life Prediction Model of Wooden Structure Based on Artificial Intelligence Algorithm

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Thinning Stentiford Algorithm for Kintamani Inscription Image Segmentation

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