论文标题
简化的基于手写的超光谱文档图像中墨水不匹配检测的基于墨水不匹配检测的方法
A Simplified Un-Supervised Learning Based Approach for Ink Mismatch Detection in Handwritten Hyper-Spectral Document Images
论文作者
论文摘要
超光谱成像已成为光学成像系统领域的最新趋势。在其他各种应用中，超光谱成像已被广泛用于分析印刷和手写文档。本文提出了一种有效的技术，用于估计超光谱文档图像中存在的不同但明显相似的墨水的数量。我们的方法基于无监督的学习，并且不需要数据集的任何先验知识。该算法在IVISION HHID数据集上进行了测试，并与文献中存在的算法状态获得了可比的结果。在超光谱文档图像中，在伪造检测的早期阶段使用时，这项工作可能是有效的。
Hyper-spectral imaging has become the latest trend in the field of optical imaging systems. Among various other applications, hyper-spectral imaging has been widely used for analysis of printed and handwritten documents. This paper proposes an efficient technique for estimating the number of different but visibly similar inks present in a Hyper spectral Document Image. Our approach is based on un-supervised learning and does not require any prior knowledge of the dataset. The algorithm was tested on the iVision HHID dataset and has achieved comparable results with the state of the algorithms present in the literature. This work can prove to be effective when employed during the early stages of forgery detection in Hyper-spectral Document Images.
