提升古老马拉地铭文图像可读性，适配石、金属与纸张材质。
Integrated Framework for Selecting and Enhancing Ancient Marathi Inscription Images from Stone, Metal Plate, and Paper Documents
- 基于二值化与互补预处理，去除污渍并增强模糊文字。
- 在石、金属板、纸本文本上分别达到65.6%、67.8%准确率。
- 适用于多种材质的古代铭文修复，适合文化遗产数字化研究者。
古老铭文图像常因老化和环境因素导致背景噪声严重、对比度低、文字模糊。许多情况下，前景文字与背景视觉特征相似，难以辨识。本文提出一种结合二值化与互补预处理技术的图像增强方法，用于去除污渍并强化不清晰的古代文字。该方法在石刻、金属板及历史文档上的古马拉地铭文图像上进行了评估。实验结果显示，使用K-Nearest Neighbor（K-NN）分类器时，石、金属板、文档类文本的分类准确率分别为55.7%、62%和65.6%；使用支持向量机（SVM）分类器时，准确率分别为53.2%、59.5%和67.8%。结果表明，该增强方法有效提升了古老马拉地铭文图像的可读性。
原文摘要 · Abstract (English)
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methods are evaluated on different types of ancient scripts, including inscriptions on stone, metal plates, and historical documents.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.
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