分析了基于LOG 算子的局部动态阈值和基于最大类间方差的全局阈值算法的基本原理，讨论了他们在机械工件数字识别中二值化的具体实现方法。针对动态阈值和全局阈值各自的优点，结合工件字符图像的特点，提出了LOG 算子的局部动态阈值和基于最大类间方差的全局阈值相结合的新方法：首先用LOG 算子的局部动态阈值算法增强字符的细节信息，较好的区分了易混淆的数字。然后利用最大类间方差的全局阈值算法对背景进行二值化。该综合算法的计算复杂度、执行时间、二值化效果以及适用范围等方面均有较佳表现，并已应用于实际系统，取得了较好的实验结果。

The basic principles of algorithm based on the LOG operator’s local dynamic threshold and the maximum between clusters variance’s global thresholds are respectively analyzed. The specific algorithm of binarization is detailedly discussed in digital recognition of mechanical workpiece. For their advantages of dynamic threshold and global threshold and in view of the characteristics of workpiece character, a new method is proposed and combines the local dynamic threshold grounded on the LOG operator with the global threshold of maximum between clusters variance. To begin with, the confusing and resemblant figures characters can be easily distinguished by using local dynamic threshold algorithm of LOG operator to enhance the details of characters. And then the binarization of image background is executed by use of the global threshold algorithm of maximum between class clusters. The integrated algorithm has good performance in many aspects such as computational complexity, implementation time, binarization effects and the scope of application. It has been applied to the actual system and achieves good results.