应用通道增强MSER与CNN的维吾尔文本区域定位

Uyghur Text Regions Localization Using Channel-Enhanced MSER and CNN

为了准确有效地定位出图像中的维吾尔文本区域,提出了一种基于通道增强最大稳定极值区域(Maximally Stable Extremal Region,MSER)和卷积神经网络(Convolutional Neural Network,CNN)的图像文本区域定位方法。应用通道增强MSER提取候选区域,根据文本特征的启发式规则以及CNN分类结果去除非文本和重复区域,通过区域融合算法得到词级别文本区域,根据该区域的色彩相近程度和空间关系召回遗漏的文本区域,并通过CNN网络对召回的区域分类融合,定位出图像文本区域。实验结果表明,该方法可以准确有效地定位文本区域,具有鲁棒性和应用性。

In order to locate Uyghur text regions in images accurately and effectively,an image text region location method based on channel enhancement Maximally Stable Extremal Region(MSER)and Convolutional Neural Network(CNN)is proposed.Channel-enhanced MSER is applied to extract candidate regions,non-text and repeated regions are removed according to heuristic rules of text features and CNN classification results,word-level text regions are obtained through a region fusion algorithm,missing text regions are recalled according to the color similarity and spatial relationship of the regions,and the recalled regions are classified and fused through CNN to locate image text regions.The experimental results show that the proposed method can locate text regions accurately and effectively,and has robustness and applicability.