Natural Scene Text Detection Based on Adaptive Color Clustering and Context Information
ZOU Bei-ji1,2, GUO Jian-jing1,2, ZHU Cheng-zhang1,2, YANG Wen-jun1,2, XU Zi-wen1,2
1. School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, China;
2. Center for Ophthalmic Imaging Research, Central South University, Changsha, Hunan 410083, China
Abstract：Natural scene text detection is an important task for image analysis and understanding.In this paper,a natural scene text detection method is proposed,using adaptive color clustering and context information analysis.Firstly,combining hierarchical clustering with self-learning strategy,we design an adaptive color clustering method,which learns clustering weights automatically and generates high character recall.Then,considering text in images usually containing several characters,we propose a character verification strategy based on image context information,which can guarantee high character recall and remove non-text components at the same time.Finally,characters are merged to text lines,and further post-processing is applied to generate final text detection results.Experiments on the ICDAR2013 publicly available dataset show that we obtain recall of 74.17%,precision of 83.40% and F-score of 78.52%.Compared with other text detection methods,our method obtains better text detection performance,indicating superiority of the proposed method.
邹北骥, 郭建京, 朱承璋, 杨文君, 徐子雯. 基于自适应色彩聚类和上下文信息的自然场景文本检测[J]. 电子学报, 2018, 46(6): 1436-1444.
ZOU Bei-ji, GUO Jian-jing, ZHU Cheng-zhang, YANG Wen-jun, XU Zi-wen. Natural Scene Text Detection Based on Adaptive Color Clustering and Context Information. Acta Electronica Sinica, 2018, 46(6): 1436-1444.
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2018, Vol. 46 
