基于改进PSENet与CRNN网络的智能电能表文本识别技术研究

电网系统的不断发展与智能化带来了庞大的计量需求，其中智能电能表作为主要计量设备得以广泛铺设，然而不同品牌、型号和批次的智能电能表携带的电能表信息也相差甚远，非智能的人工信息采集方式已经严重阻碍了电能表设备升级发展与采集安全，制约了电力资产管理的质量和水平。文中将文本识别技术应用于智能电能表的信息采集过程，设计一种两阶段的系统对电能表图片中的文本信息进行检测并识别，实现了电能表信息智能化采集，提高了智能电能表信息提取的效率和安全性。文中的两阶段系统包括文本检测模块和文本识别模块，文本检测模块通过改进的PSENet网络对电能表图片中的文本位置进行检测，文本识别模块通过CRNN网络对检测到的文本框进行识别。算法本身不受输入图像的质量和场景束缚，并且对面临的字体大小不一、曝光过高或过低等问题具有较强的抗干扰能力，对电能表图片中的汉字、英文和数字都具有很高的识别精度。

Research on Scene Text Recognition Technology of Smart Meter

The continuous development and intelligence of the power grid system have brought huge measurement needs and smart meters are widely laid as the main measurement equipment. However, the information of the meters carried by smart meters of different brands, models and batches is also very different. Non-intelligent artificial information collection has seriously hindered the upgrading and development of measurement infrastructure and collection security, and restricted the quality and level of power asset management. In this paper, the text recognition technology is applied to the information collection process of smart meters. Design a two-stage system to detect and identify the text information in the photos of the meters, which realizes intelligent collection of meter information and improves the efficiency and safety. The two-stage system in this paper includes a text detection module and a text recognition module. The text detection module detects the text position in the meter picture through the improved PSENet network, and the text recognition module recognizes the detected text box through the CRNN network. The algorithm itself is not constrained by the quality of the input image and the scene, and it has strong anti-interference ability to the problems of different font sizes, too high or too low exposure for text detection and recognition in smart meters. English and numbers have high recognition accuracy. And the recognition accuracy for Chinese characters, English and numbers in the meter picture is very high.