In this paper, we present a segmentation-free word spotting method based on Wave Kernel Signature (WKS) under the foundation of quantum mechanics. The query word and the document page are smoothened first, then SIFT detector is used to obtain the keypoints in both the query image and the document page. A window is placed centered at each keypoint to obtain the WKS descriptors. The WKS descriptors represent the average probability of measuring a quantum mechanical particle at a specific location based on quantum energy. We use an efficient search technique which calculates minimum energy difference between query word and document image to spot where the query word appears in the document image. The proposed method is tested on three historical Bangla handwritten datasets, one Bangla handwritten dataset, one old Bangla-printed dataset and one historical English handwritten dataset. To substantiate the goodness of the proposed method, its performance is measured using standard metrics.

中文翻译：

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使用Wave Kernel签名在孟加拉国历史手写文档中实现无分割的单词识别

本文在量子力学的基础上，提出了一种基于Wave Kernel Signature（WKS）的无分割词发现方法。首先对查询词和文档页面进行平滑处理，然后使用SIFT检测器获取查询图像和文档页面中的关键点。在每个关键点的中心放置一个窗口，以获取WKS描述符。WKS描述符表示基于量子能量在特定位置测量量子机械粒子的平均概率。我们使用一种高效的搜索技术来计算查询词与文档图像之间的最小能量差，以找出查询词在文档图像中出现的位置。在三个历史孟加拉语手写数据集，一个孟加拉语手写数据集，一个古老的孟加拉语印刷数据集和一个历史英语手写数据集。为了证实所提出方法的优越性，使用标准指标对其性能进行了测量。