As a precursor of optical character recognition (OCR) technology, script identification finds many applications like sorting and indexing of document images. Classifying these scripts, especially at different scales and orientations, is one of the interesting and vital problems in the field of document image analysis. In this paper, an algorithm is proposed for the identification of scripts using scale and rotation robust log-polar wavelet and semi decimated wavelet features. Initially, words are segmented from document images in the form of text-blobs by the Gaussian filter. Then, texture features are calculated using a combination of discrete wavelet and semi decimated discrete wavelet transforms in log-polar domain. Here, most of the rotational and scale variations are removed in log-polar domain, whereas wavelet transform is capable of extracting the information at different resolution levels. This helps in the formation of significant textures for the purpose of characterization. At last, k-nearest neighbor classifier is used for the identification of scripts. Comprehensive experiments on different databases illustrate the effectiveness of the proposed algorithm. Benchmarking analysis shows that a maximum recall rate of 98.96% is obtained, and demonstrates better performance compared to the other contemporary approaches.

脚本识别作为光学字符识别（OCR）技术的前身，发现了许多应用程序，例如文档图像的分类和索引。对这些脚本进行分类，尤其是对不同比例和方向的脚本进行分类，是文档图像分析领域中有趣且至关重要的问题之一。本文提出了一种基于尺度和旋转鲁棒对数极小波和半抽取小波特征的脚本识别算法。最初，单词是通过高斯滤波器以文本斑点的形式从文档图像中分割出来的。然后，使用对数极化域中的离散小波和半抽取离散小波变换的组合来计算纹理特征。在这里，大多数旋转和比例变化都在对数极域中消除，而小波变换能够提取不同分辨率级别的信息。为了表征的目的，这有助于形成明显的纹理。最后，k-最近邻居分类器用于识别脚本。在不同数据库上的综合实验说明了该算法的有效性。基准分析显示，与其他当代方法相比，最大召回率达到98.96％，并且表现出更好的性能。