摘要
针对复杂背景下汉字匹配准确率较低的问题,提出一种改进的SURF算法。该算法利用灰度分级的字符分割方法,先进行灰度分割增强图像的对比度,采用灰度分级树将图像中的所有像素处理为树的模式进行计算,根据灰度分级确定主节点,根据主节点的级别所对应的灰度值对图像进行分割。同时,根据汉字结构的特殊性,取消了SURF算法的旋转不变性。实验结果表明,与未使用改进的SURF算法相比,对图像质量较差的文本图像,改进的SURF算法能有效地提高其匹配的准确率。
Aiming at the low matching accuracy of Chinese characters, an improved algorithm of SURF is presented. The algorithm is based on gradation character segmentation. Contrast of image is enhanced by using gray level segmentation, and then with the gray level classification tree, all pixels in the image are processed to the tree model. According to the gray level classification, the main node is determined. Grey level corresponding to the main node level is used in image segmentation. According to the particularity of Chinese characters, the rotation invariance of SURF algorithm is cancelled. Experimental results show that the improved algorithm can improve the matching accuracy effectively, especially for text image of poor quality.
出处
《计算机工程与应用》
CSCD
北大核心
2015年第12期156-160,共5页
Computer Engineering and Applications
基金
中央高校基本科研业务费专项资金资助(No.TD2014-01)
关键词
复杂背景
汉字匹配
快速鲁棒特征(SURF)算法
灰度分级
字符分割
complex background
Chinese characters matching
Speeded-Up Robust Features （SURF） algorithm
gray classification
character segmentation
