摘要
针对复杂背景下的文本检测问题,提出了一种基于主动轮廓模型的文本检测方法。输入的图像首先经过sobel-laplacian锐化后再由gaussian-laplacian进行过滤。预处理完成后的图片首先通过改进的主动轮廓模型得到初始轮廓,再通过算法的反复迭代扩大或缩小轮廓线得到最终轮廓,最后通过后处理尽量排除非文本块,从而得到最终文本区。区别于以往检测方法,所提方法最终不但可以框出文本行,还可以框出单个文本,有利于后续分割识别的进行。实验表明所提方法可有效检测出图像中的文本。
To detect text from images with different backgrounds, a text detection method with active contour models was proposed. Before text detection, the sobel-laplacian and gaussian-laplacian were used to sharpen edges and smooth noise, and then iteration algorithm was repeated to enlarge or lessen the contour to get the final contour, ruling out the un-text block at last. The proposed method can box a single text eventually, and it is advantageous to the subsequent segmentation recognition. Experiment shows that the proposed method can effectively detect the text in the image.
出处
《计算机科学》
CSCD
北大核心
2015年第6期288-292,共5页
Computer Science
基金
国家自然科学基金项目(61302157)资助
关键词
文本检测
主动轮廓模型
复杂背景
边缘检测
Text detection
Active contour models（ACM）
Complex background
Edge detection
