大数据环境下实验室局域网络含噪字符识别模型设计

Design of Noisy Character Recognition Model in Laboratory Local Area Network Under Big Data Environment

为了提高大数据环境下实验室局域网络含噪字符的识别能力,提出基于视觉信息增强的实验室局域网络含噪字符识别模型.采用关键特征点提取方法进行实验室局域网络含噪字符的特征检测,进行实验室局域网络含噪字符的边缘轮廓特征检测,结合小波多级降噪方法进行实验室局域网络含噪字符的降噪处理,对实验室局域网络含噪字符的关键特征点进行标记,结合图像信息增强处理技术实现实验室局域网络含噪字符的识别.仿真结果表明,采用该方法进行实验室局域网络含噪字符识别的精度较高,特征分辨能力较好,输出信噪比较高.

In order to improve the recognition ability of noisy characters in lab LAN under big data environment,a recognition model of noisy characters in lab LAN based on visual information enhancement is proposed.The key feature points extraction method is used to detect the features of the noisy characters in the laboratory LAN,and the edge contour features of the noisy characters in the laboratory LAN are detected.The denoising processing of the noisy characters in the laboratory LAN is carried out with the wavelet multi-level noise reduction method.The key feature points of the noisy characters in the laboratory LAN are marked,and the image information enhancement processing technology is used To realize the recognition of noisy characters in laboratory LAN.The simulation results show that the method has high accuracy,good feature resolution and high output signal-to-noise ratio.