同态增晰耦合改进的区域生长的键盘字符目标识别算法

Keyboard character identification algorithm based on homomorphic clarification and improved region growing

键盘作为诸多智能设备指令输入端口,其表面字符完整程度直接影响到设备使用。为了提高键盘在制造过程中的生产质量,需要对其生产流水线完成视觉检测。而在当前键盘表面字符目标检测过程中存在成像模糊、分割不准确以及识别率低等问题。对此,设计基于同态增晰与区域生长的键盘按键识别检测算法。首先,引入同态增晰算法,对模糊图像清晰化处理。然后嵌入全局特征,改进了区域生长算法,准确分割并提取出图像中数字按键目标区域。最后基于最近邻算法对数字按键图像样本库进行机器学习,完成按键数字识别,从而建立起键盘数字按键质量检查系统。实验数据显示,与当前数字识别算法相比,面对成像模糊的按键数字图像时,该数字按键检查算法具备更高的准确性与稳定性。

The keyboard is the instructions input port of various intelligent devices,and its surface characters complete degree can directly affect on device use. In order to improve the production quality of the keyboard in manufacturing process,it is necessary to complete the visual inspection of the production line. There are problems of blurred imaging,inaccurate segmentation and low recognition rate in the current target detection of keyboard surface characters. To solve the above problems,the keyboard＇s key identification and detection algorithm based on homomorphic clarification and region growing was designed. Firstly the homomorphic clarification algorithm is introduced to do clear processing of fuzzy image. And then the global feature is embedded to improve the region growing algorithm,and accurately segment and extract the target region of number keys in the image.Finally,the machine learning for the image sample library of number key is conducted based on nearest neighbor algorithm（NNA）to identify the key number and establish the quality inspection system for the number keys on keyboard. The experimental data shows that,in comparison with the current number recognition algorithm,the proposed number key inspection algorithm has higher accuracy and stability while identifying the key＇s number image with blurred imaging.