标牌粘连字符自适应定位分割重建与识别

Merged characters segmentation reconstruction and recognition of labels based on adaptive location

目的针对仪表、电梯等标牌上一些字符间距较小,传统分割方法分割不准确,字符识别率不高的问题,提出了一种标牌粘连字符自适应定位分割重建识别算法。方法首先对标牌图像进行中值滤波、二值化等预处理;其次运用数学形态学方法对预处理后的图像进行开运算及腐蚀,将字符间一些无用的信息去掉,增大字符间距;继而通过形心算法找出每个字符的几何中心,并通过Sobel边缘检测算子根据几何中心获取每个字符边框,建立ROI(region of interest),再返回标牌原图利用已经建立的ROI从中分割字符,依据国家字符间距相关标准,在分割的每个字符后加一定像素宽的矩形间隔条后重建字符图像,再进行OCR(optical character recognition)字符识别。结果经过对993块标牌进行字符识别实验,算法的识别率达到95.7%。结论实验结果表明本文算法是对标牌字符识别的一种有效算法。

Objective To solve the problem of inaccurate segmentation and low character recognition on the instrument labels of small character spacing by traditional methods, a recognition algorithm is proposed for merged characters through adaptive segmentation and reconstruction. Method First, we use algorithms, such as median filtering and binarization, for image pre-processing. Second, the morphology filter is used to remove unwanted messages and to increase character spacing. Third, we find the geometric center of each character through centroid algorithm, getting each character bounding by the Sobel edge detection operator, and establish the Region of Interest （ROI） of each character. Then turn to the original image for character segmentation, and add certain pixels wide rectangular spacing bar, which refers to the GB standards to each character after segmentation, and then we reconstruct the image. Finally, we carry on the Optical Character Recognition （OCR） . Result The character recognition experiments for 993 instrument labels with a recognition rate of 95.7% , Conclusion The algorithm proposed was proved for an effective character recognition method for instrument labels.