一种古籍汉字图像的多属性模糊检索模型

针对古籍汉字结构复杂、风格多变以及字形图像降质所导致传统文字图像检索技术在应用于古籍汉字图像时性能不佳的问题,引入犹豫模糊集理论,提出了一种古籍汉字图像检索模型.首先,设计面向古籍汉字图像的重叠模糊规范化双弹性网格划分,通过考察当前网格与其近邻网格间各种字形要素间的几何和统计特征,定义相应的犹豫模糊元素,进而构成古籍汉字查询图像和目标图像的犹豫模糊集合;其次,以犹豫模糊集合的加权距离测度作为古籍汉字查询图像和目标图像的相似性测度,得到古籍汉字图像检索结果的有序输出.本文算法在11 574幅古籍汉字图像上的检索查准率和查全率分别为78.9%和76.5%.

A multi-attribute fuzzy retrieval model for ancient Chinese charater images

The traditional character image retrieval technology was difficult to meet the needs of users when it was applied to retrieve ancient Chinese character images because of the complex structure of ancient Chinese characters, changing of font styles, and degradation of font images. To address this problem, a multi-attribute fuzzy retrieval model of Chinese characters in ancient books was proposed by introducing the theory of hesitant fuzzy set. Firstly, an overlapping fuzzy normalized Bi elastic mesh generation for Chinese character images of ancient books was designed. A hesitant fuzzy set containing the query images and target images was established by investigating the geometric and statistical characteristics of various glyph elements between the current grid and its adjacent grids, and defining corresponding hesitant fuzzy elements. Secondly, the weighted distance measure of hesitant fuzzy sets was used to the similarity measure between the query image and the target image, then obtaining the orderly output of the retrieval results. The precision rate and recall rate of retrieval experiments on 11 574 ancient Chinese character images are 78.9% and 76.5%, respectively.