基于SIFT图像配准的维吾尔语文字识别方法

Uyghur character recognition method based on SIFT image registration

提出一种新的维吾尔语文字识别研究方法。首先,建立字符样本库,并对库中文字图像归一化。然后,将测试图像与样本图像进行垂直和水平双方向投影相关性检测,对与测试图像双投影相关性较高的样本字符进行笔画数特征提取,得到预分类结果。最后,将测试图像与预分类结果进行SIFT关键点检测、方向描述子生成与配准,与测试图片匹配点对最多的预分类结果为识别结果,并输出该结果标记符号对应的维吾尔语字符。实验结果表明:该方法能减少字符样本的数量,并有效解决测试图像尺度与几何形变的差异造成的匹配困难问题。

A new research method for Uyghur character recognition is proposed.Firstly,a character sample corpus is built and all characters and images in it are normalized.Then,correlation detection on both horizontal and vertical directions projections of test images and sample images are carried out,and then extract stroke number feature of sample images which have higher correlation with double projection of test image to get preclassification result.Finally,detect SIFT key points of test image and pre-classification results,generate orientation descriptors and registrate and pre-classification results with the highest matching number points are regarded as recognition result and output result notation Uyghur character.Experimental results show that this method can reduce number of characters sample and effectively solve difficulty caused by difference of scale and geometric distortion of test image.