基于机器视觉的点阵字符分割方法

Segmentation Method of Dot Matrix Character based on Machine Vision

针对点阵喷码字符因背景复杂、局部漏喷或字符模糊等原因导致字符识别率低的问题,提出了一种基于机器视觉的点阵字符分割方法。首先,利用Blob分析从图像中快速定位出点阵字符所在矩形区域,并将其旋转到水平方向;随后,采用水平灰度投影法将该矩形区域分割成两行,再采用垂直灰度投影法结合字符宽度特征将每行中的字符分割开来;最后,将分割出的字符送入MLP分类器进行识别。实验结果显示,该方法的分割准确率为98.7%,显著优于现有的分割方法,且还具有较强的识别通用性。因此,该方法在点阵字符识别方面具有良好的应用价值。

A dot matrix character segmentation method based on machine vision was proposed to address the low recognition rate of dot matrix characters caused by complex backgrounds,local leakage,or blurred characters.First,the rectangular area where the dot matrix characters were located was quickly located from the image using Blob analysis and rotated to the horizontal direction.Then,the rectangular area was segmented into two rows using horizontal grey scale projection,and the characters in each row were segmented using vertical grey scale projection combined with character width features.Finally,the segmented characters were fed into the MLP classifier for recognition.The experimental results show that the segmentation accuracy of the method is 98.7%,which is significantly better than existing segmentation methods,and also has strong recognition generality;therefore,the method has good application value in the recognition of dot matrix characters.