Zagoris Konstantinos, Amanatiadis Angelos, Pratikakis Ioannis
Department of Computer Science, Neapolis University, Pafos 8042, Cyprus.
Department of Production and Management Engineering, Democritus University of Thrace, 67132 Xanthi, Greece.
J Imaging. 2021 Dec 17;7(12):278. doi: 10.3390/jimaging7120278.
Word spotting strategies employed in historical handwritten documents face many challenges due to variation in the writing style and intense degradation. In this paper, a new method that permits efficient and effective word spotting in handwritten documents is presented that relies upon document-oriented local features that take into account information around representative keypoints and a matching process that incorporates a spatial context in a local proximity search without using any training data. The method relies on a document-oriented keypoint and feature extraction, along with a fast feature matching method. This enables the corresponding methodological pipeline to be both effectively and efficiently employed in the cloud so that word spotting can be realised as a service in modern mobile devices. The effectiveness and efficiency of the proposed method in terms of its matching accuracy, along with its fast retrieval time, respectively, are shown after a consistent evaluation of several historical handwritten datasets.
由于书写风格的变化和严重的退化，历史手写文档中采用的单词识别策略面临诸多挑战。本文提出了一种新方法，该方法依赖于面向文档的局部特征（考虑代表性关键点周围的信息）和匹配过程（在局部邻近搜索中纳入空间上下文，且不使用任何训练数据），从而能够在手写文档中高效且有效地进行单词识别。该方法依赖于面向文档的关键点和特征提取，以及快速特征匹配方法。这使得相应的方法管道能够在云端有效且高效地应用，从而使单词识别能够在现代移动设备中作为一种服务得以实现。在对多个历史手写数据集进行一致评估后，分别展示了所提方法在匹配准确性方面的有效性以及快速检索时间方面的效率。
