Lin Huei-Yung, Hsu Chin-Yu
Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106, Taiwan.
Department of Electrical Engineering, National Chung Cheng University, Chiayi 621, Taiwan.
Entropy (Basel). 2023 Apr 14;25(4):658. doi: 10.3390/e25040658.
The detection of regions of interest is commonly considered as an early stage of information extraction from images. It is used to provide the contents meaningful to human perception for machine vision applications. In this work, a new technique for structured region detection based on the distillation of local image features with clustering analysis is proposed. Different from the existing methods, our approach takes the application-specific reference images for feature learning and extraction. It is able to identify text clusters under the sparsity of feature points derived from the characters. For the localization of structured regions, the cluster with high feature density is calculated and serves as a candidate for region expansion. An iterative adjustment is then performed to enlarge the ROI for complete text coverage. The experiments carried out for text region detection of invoice and banknote demonstrate the effectiveness of the proposed technique.
感兴趣区域的检测通常被视为从图像中提取信息的早期阶段。它用于为机器视觉应用提供对人类感知有意义的内容。在这项工作中，提出了一种基于局部图像特征聚类分析的结构化区域检测新技术。与现有方法不同，我们的方法采用特定应用的参考图像进行特征学习和提取。它能够在字符衍生的特征点稀疏的情况下识别文本聚类。对于结构化区域的定位，计算具有高特征密度的聚类并将其用作区域扩展的候选。然后进行迭代调整以扩大感兴趣区域以实现完整的文本覆盖。针对发票和钞票的文本区域检测进行的实验证明了所提出技术的有效性。
