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Clustering Document Fragments using Background Color and Texture Information

机译:使用背景颜色和纹理信息对文档片段进行聚类

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摘要

Forensic analysis of questioned documents sometimes can be extensively data intensive. A forensic expert might need to analyze a heap of document fragments and in such cases to ensure reliability he/she should focus only on relevant evidences hidden in those document fragments. Relevant document retrieval needs finding of similar document fragments. One notion of obtaining such similar documents could be by using document fragment's physical characteristics like color, texture, etc. In this article we propose an automatic scheme to retrieve similar document fragments based on visual appearance of document paper and texture. Multispectral color characteristics using biologically inspired color differentiation techniques are implemented here. This is done by projecting document color characteristics to Lab color space. Gabor filter-based texture analysis is used to identify document texture. It is desired that document fragments from same source will have similar color and texture. For clustering similar document fragments of our test dataset we use a Self Organizing Map (SOM) of dimension 5×5, where the document color and texture information are used as features. We obtained an encouraging accuracy of 97.17% from 1063 test images.
机译:对可疑文档进行取证分析有时可能需要大量数据。法医专家可能需要分析大量文件碎片,在这种情况下,为了确保可靠性,他/她应仅关注那些文件碎片中隐藏的相关证据。相关文档检索需要找到相似的文档片段。获取此类相似文档的一个想法是通过使用文档碎片的物理特性(例如颜色,纹理等)。在本文中,我们提出了一种基于文档纸张外观和纹理的自动方案来检索相似文档碎片。使用生物学启发的颜色区分技术实现多光谱颜色特性。这是通过将文档颜色特征投影到Lab颜色空间来完成的。基于Gabor过滤器的纹理分析用于识别文档纹理。希望来自相同来源的文档片段将具有相似的颜色和纹理。为了将测试数据集的相似文档片段聚类,我们使用尺寸为5×5的自组织图(SOM),其中文档颜色和纹理信息用作特征。我们从1063张测试图像中获得了令人鼓舞的97.17%的准确性。

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