目的 为了实现电动滑板车包装前液晶屏字符检测高效、高精度的目标，以及为了解决液晶屏字符中Led段码字体难以精确分割、匹配算法复杂等问题。方法 通过Hough直线检测实现字符区域的位置校正，投影法实现分割字符区域，形态学处理、连通域分析实现各字符的提取，采用BP神经网络模型对字符进行识别，最后通过改进的几何特征检测字符缺线、漏线，灰度特征检测字符亮度不均匀。结果 液晶屏字符实验结果表明，每个字符平均识别时间为0.16 s，每个屏幕平均识别时间为0.6 s，液晶屏字符缺陷加权识别率为96%。结论 该算法具有较高的可靠性、效率、识别率，解决了液晶屏字符在几何、亮度缺陷下高效、高精度检测实际工程的问题，为同类产品的检测提供了算法经验。

The work aims to achieve the high efficiency and high precision of LCD character detection before the packaging of electric scooters, and to solve the problems such as the difficulty of accurate segmentation and complex matching algorithm of Led segment code fonts in LCD characters. Hough line detection was conducted to correct the character region. Projection method was used to segment the character region. Morphological processing and connected domain analysis were used to extract the characters. BP neural network model was used to recognize the characters. Finally, improved geometric features were used to detect the lacking and missing lines. Gray scale features were used to detect the uneven brightness of the characters. The experimental results of LCD characters showed that the average recognition time of each character was 0.16 s and that of each screen was 0.6 s. The weighted recognition rate of LCD character defects was 96%. The algorithm has high reliability, efficiency and recognition rate, and solves the practical engineering problems of high efficiency and high precision detection of LCD characters under the defects of geometry and brightness, and provides the algorithm experience for the detection of similar products.