Abstract: Printing label is the main way to monitor the state of ink balance. As the control for printing ink balance mainly depends on human experience, there are some problems such as strong subjectivity and low efficiency. In this study, the digital image processing and pattern recognition algorithm are used to solve the problem mentioned above and realize the automatic monitoring and identification of the balance of ink and water. The feature of printing label is abstracted with a 2D-Gabor filter and analyzed with a principal component analysis. SVM nets are trained to identify the statement of water and ink with the result of PCA and it has a qualified accuracy in application. This study has a significant in reducing the debugging time and cost of equipment. Both the quality and automation degree of printing will be improved.

Key words: printing label, 2D-Gabor filtering, ink-water balance, Support Vector Machine（SVM）, Principal Component Analysis（PCA）

摘要： 印刷星标是判断水墨平衡的重要方式，由于其同时受到水墨关系与光照等因素影响，难以通过一般灰度分析方法准确判断，故提出通过图像纹理识别星标，以实现水墨状态的自动判断。内容包括印刷星标的采集与预处理，基于2D-Gabor的印刷星标纹理特征提取与融合PCA分析的纹理特征集降维，最后利用SVM实现决策网络的构建，实现了印刷机工作中对星标的自动识别，相关研究结果在减少调试维修设备工时和成本、提高印刷调节效率、快速实现水墨平衡等方面具有实际应用价值。

关键词: 印刷星标, 二维Gabor滤波, 水墨平衡, 支持向量机, 主成分分析