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methods are evaluated on different types of ancient scripts, including inscriptions on stone, metal plates, and historical documents.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.

翻译：古代文字图像常因年代久远及环境影响而存在严重背景噪声、低对比度及退化问题。在许多情况下，前景文字与背景呈现相似的视觉特征，导致铭文难以辨识。图像增强的主要目标在于提升此类退化古图像的可读性。本文提出一种基于二值化与互补预处理技术的图像增强方法，用于去除污迹并增强模糊的古代文字。所提方法在多种古代文字类型上进行了评估，包括石质铭文、金属板铭文及历史文献。实验结果表明，使用K最近邻（K-NN）分类器时，所提方法对石质、金属板及文献文字的分类准确率分别达到55.7%、62%与65.6%；使用支持向量机（SVM）分类器时，准确率分别为53.2%、59.5%与67.8%。这些结果证明了所提增强方法在提升古马拉地语铭文图像可读性方面的有效性。