国防科技大学学报2017，Vol.39Issue(3)：91-96,6.DOI:10.11887/j.cn.201703015
用卷积神经网络分类最大稳定极值区域实现汉字区域定位
Scene Chinese text localization by convolutional neural network classifying maximum stable extremal regions
摘要
Abstract
Firstly,the MSERs (maximum stable extremal regions) which corresponded to Chinese strokes was extracted.The morphological close operation was used to connect the nearby MSERs.The fused MSER corresponded to Chinese characters.Gray level co-occurrence matric was used to describe the textural characteristics of the fused MSER rectangle.They were the input of CNN (convolutional neural network).The MSER rectangles were classified by CNN in order to filter none Chinese character rectangle.Then,Chinese text candidates were constructed by clustering MSER rectangles based on the features such as the color histogram Bhattacharyya distance of MSER rectangles.CNN was reused to classify Chinese text candidates to filter none Chinese text clusters.Finally,the rectangle of the remaining clusters was the Chinese text regions of natural scene image.Experiment shows that the proposed algorithm is desirable in localizing the Chinese text in natural scene images.关键词
汉字区域定位/最大稳定极值区域/卷积神经网络/深度学习/灰度共生矩阵Key words
Chinese text localization/maximum stable extremal region/convolutional neural network/deep learning/gray level co-occurrence matric分类
信息技术与安全科学引用本文复制引用
张鹏伟,张伟伟..用卷积神经网络分类最大稳定极值区域实现汉字区域定位[J].国防科技大学学报,2017,39(3):91-96,6.基金项目
国家863计划资助项目(20157011012) （20157011012）
