基于边缘增强MSER的模糊检务文本检测

Fuzzy inspection text detection based on edge enhancement MSER

摘要:

为了可以从模糊检务图像中高效、准确地提取文字信息,本文提出了基于边缘增强的最大稳定极值区域(maximally stable extremal regions, MSER)算法和免疫遗传(immunogenetic algorithm,IGA) 优化支持向量机(support vector machine, SVM)的多特征自适应权重融合相结合的方法来提取模糊检务图像中的文本。利用边缘增强的MSER算法对图像文本进行检测,将所检测出的MSER进行合并得到文字候选区域；为了滤除候选区域中的非文本区域,采用特征融合公式对图像的3种特征进行融合,然后采用IGA优化SVM分类器寻找最优参数,最后将候选区域送入训练好的分类器滤除非文本。实验结果表明,相较于其他算法,本文算法有更高的真阳率与更低的假阳率,针对模糊检务图像文字提取具有更高的准确性。

Abstract:

In order to extract the text information from fuzzy inspection images efficiently and accurately,this paper proposes a combination of maximally stable extremal regions (MSER) algorithm based on edge enhancement and multi-feature adaptive weight fusion with immunogenetic (IGA) optimization support vector machine (SVM) to extract text from fuzzy inspection images.The edge-enhanced MSER algorithm is used to detect the image text,and merge the detected MSERs to get the text candidate regions.In order to filter out non-text region in the candidate regions,the feature fusion formula is used to fuse the three features of the image,then the IGA is used to optimize the SVM classifier to find the optimal parameters,and finally the candidate regions are fed into the trained classifier to filter out non-text.The experimental results show that the algorithm has higher true positive rate and lower false positive rate compared with other algorithms,higher accuracy for fuzzy inspection image text extraction.