基于边界约束图像融合的光学字符识别算法研究

为了提高非合作目标区域中光学字符的识别能力，增强电力铭牌、电网文本等信息采集的准确性，设计了一种偏振图像与可见光图像同时采集并进行图像融合的光学字符识别系统。通过设置0°、60°和120°的偏振角对响应电压进行周期性调制，从而获取有效信息区域的连通范围，实现准确的边界约束。通过计算偏振角度标定参数，设置边界条件的合理阈值，为图像融合提供范围标准。实验分别测试了响应电压关于偏振角度和测试距离的函数曲线，结果显示，偏振角度周期性变化斜率为53.1 mV/(°)。在0.5~3.0 m范围内，响应电压最大值为241.7 mV，最小电压为18.5 mV，并且三条响应曲线的单调性几乎一致。实验针对图像清晰度较差的电力铭牌目标进行测试，结果显示，模糊的原始图像在传统图像滤波与增强后，对比度从0.34提升至1.56，图像质量得到了一定的改善，但仍有部分字符无法识别。而采用文中算法后，对比度达到了3.23，部分模糊字符也能有效识别。可见，该系统适用于非合作目标的光学字符识别，对低质量图像中光学字符识别具有很好的优化效果。

Research on optical character recognition algorithm based on boundary constrained image fusion

In order to improve the recognition ability of optical characters in non-cooperative target areas, and enhance the accuracy of information collection such as power nameplates and power grid texts, an optical character recognition system was designed that simultaneously collected polarized images and visible light images and performed image fusion. By setting the polarization angles of 0°, 60° and 120°, the response voltage was periodically modulated to obtain the connectivity range of the effective information region and achieve accurate boundary constraints. By calculating the polarization angle calibration parameters and setting reasonable thresholds of boundary conditions, a range standard was provided for image fusion. The function curves of the response voltage on the polarization angle and the test distance were tested in the experiment, and the results showed that the slope of the periodic change of the polarization angle was 53.1 mV/(°). In the range of 0.5-3.0 m, the maximum value of the response voltage was 241.7 mV, the minimum voltage was 18.5 mV, and the monotonicity of the three response curves was almost the same. The experiment was carried out on the power nameplate target with poor image definition. The results showed that after traditional image filtering and enhancement, the contrast ratio of the blurred original image was increased from 0.34 to 1.56, and the image quality was improved to a certain extent, but there were still some characters that could not be identified. After using this algorithm, the contrast ratio reaches 3.23, and some fuzzy characters can also be effectively recognized. It can be seen that the system is suitable for optical character recognition of non-cooperative targets, and has a good optimization effect on optical character recognition in low-quality images.