Graphic-based character grouping in topographic maps

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• 作者：Pengfei Xu^a ; ¹ ; ^{pfxu@nwu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Qiguang Miao^b ; ^{qgmiao@xidian.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; ^{qgmiao@126.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Tian׳ge Liu^b ; ² ; ^{liutiange@126.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Xiaojiang Chen^a ; ³ ; ^{xjchen@nwu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Weike Nie^a ; ⁴ ; ^{onetwo3817@sohu.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Character grouping ; Undirected graph ; Graphic processing ; The properties of the nodes ; The weights of the edges ; Topographic map
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：12 May 2016
• 年：2016
• 卷：189
• 期：Complete
• 页码：160-170
• 全文大小：7157 K

文摘

In topographic maps, only the complete text strings can accurately express the properties of the geographic elements, so individual characters should be grouped into text strings before recognition. This paper presents a novel character grouping method based on the graph model. In this method, undirected graphs are used to describe different words, where the color and size of the characters are served as the properties of the nodes, while the distance and angle between the characters are served as the weights of the edges connecting pairs of characters. Therefore, the nodes can be connected to construct undirected graphs according to their properties. Then the constructed graphs are simplified according to the weights of the edges. Finally, we can get the final results corresponding to the grouped characters. Experimental results show that this method can especially group the characters with significant wide spacing. Moreover, it has higher efficiency with graphic processing instead of image processing.