图像中多语种文本提取的高斯混合建模方法

Gaussian Mixture Modeling of Neighbor Characters for Multilingual Text Extraction in Images

建立了相邻字符区域的高斯混合模型,用于区分字符与非字符.在此基础上,提出了一种从图像中提取多语种文本的方法.首先对输入图像进行二值化,并执行形态学闭运算,使二值图像中每个字符成为一个单独的连通成分.然后根据各连通成分重心的Voronoi区域,形成连通成分之间的邻接关系;最后在贝叶斯框架下,基于相邻字符区域的高斯混合模型计算相应的伪概率,以此为判据将每个连通成分标注为字符或非字符.利用所提出的文本提取方法,进行了复杂中英文文本的提取实验,获得大于97%的准确率和大于80%的召回率,证实了方法的有效性.

A new method based on the Gaussian mixture modeling of neighbor characters is proposed to extract multilingual texts in images. In the training phase, the Gaussian mixture model of three neighbor characters is trained from the examples. Then the texts in an input image are extracted in the following steps. Firstly, the image is binarized using the edge-pixel clustering method and the morphological closing operation is performed on the binary image, in order that each character in it can be treated as a connected component. Secondly, the neighborhood of connected components is established according to the Voronoi partition of the image. Three connected components neighboring with each other constitute a neighbor set. For each neighbor set, a posteriori pseudo-probability is computed based on the Gaussian mixture model of three neighbor characters and used to classify the neighbor set as the case of three neighbor characters. Finally, the text extraction is completed by labeling the connected components as characters or non- characters with the following rule： if a connected component is included in at least one neighbor set classified as the case of three neighbor characters, then the connected component is labeled as a character, or else as a non-character. The proposed method are tested in the applications of Chinese and English text extraction. In the experiments, the expectation-maximization algorithm is employed to train the Gaussian mixture model of three neighbor characters. The experimental results of text extraction show the effectiveness of the method.