Abstract
Ancient script images often suffer from severe background noise, low contrast, and degradation caused by aging and environmental effects. In many cases, the foreground text and background exhibit similar visual characteristics, making the inscriptions difficult to read. The primary objective of image enhancement is to improve the readability of such degraded ancient images. This paper presents an image enhancement approach based on binarization and complementary preprocessing techniques for removing stains and enhancing unclear ancient text. The proposed methods are evaluated on different types of ancient scripts, including inscriptions on stone, metal plates, and historical documents. Experimental results show that the proposed approach achieves classification accuracies of 55.7%, 62%, and 65.6% for stone, metal plate, and document scripts, respectively, using the K-Nearest Neighbor (K-NN) classifier. Using the Support Vector Machine (SVM) classifier, accuracies of 53.2%, 59.5%, and 67.8% are obtained. The results demonstrate the effectiveness of the proposed enhancement method in improving the readability of ancient Marathi inscription images.
Abstract (translated)
古代文字图像常常受到严重背景噪声、低对比度以及由于老化和环境因素导致的退化问题的影响。在许多情况下，前景文本与背景具有相似的视觉特征，这使得这些刻文难以阅读。图像增强的主要目标是改善此类受损古文字图像的可读性。 本文提出了一种基于二值化和互补预处理技术的方法，用于去除污渍并增强模糊不清的古代文本。所提出的这种方法在不同类型的历史文献上进行了评估，包括石刻、金属板以及历史文件上的铭文。实验结果表明，在使用K-Nearest Neighbor（K-NN）分类器时，对于石刻、金属板和文档脚本分别获得了55.7%、62% 和 65.6% 的分类准确性；在使用支持向量机（SVM）分类器时，则分别达到了53.2%、59.5% 和 67.8% 的准确率。这些结果证明了所提出增强方法的有效性，特别是在改善古代马拉地语铭文图像的可读性方面。 通过这种方法，可以更有效地对古文字进行数字化和研究，为历史学家提供更加清晰的研究材料。
URL
https://arxiv.org/abs/2601.04800
