基于卷积神经网络的电力操作票文字识别方法

为提升电力操作票文字识别的准确度,提出了一种CBTR(基于卷积神经网络的文字识别)方法。首先,基于卷积神经网络学习得到非线性映射函数,提升操作票图像的PSNR(峰值信噪比);然后,基于假想笔画、路径签名与8方向特征构建集成卷积神经网络模型,使用简单平均法计算分类结果,克服手写字体识别难题。最后,选择DLQDF, MCDNN, DeepCNet作为基准方法,使用实际运维检修中操作票图像样本集进行算法验证。结果表明CBTR方法能准确识别操作票图像文字,具有显著的性能优势。

Text Recognition of Power Operation Tickets Based on Convolutional Neural Network

To improve the accuracy of text recognition in power operation tickets, the paper proposes a convolutional neural network-based text recognition method(CBTR). Firstly, CBTR learns a non-linear mapping function based on the convolutional neural network to improve the peak signal-to-noise ratio(PSNR) of the ticket images. Secondly, an integrated convolutional neural network model is constructed based on imaginary strokes, path signatures, and 8-direction features. The classification result is calculated by a simple average method to recognize handwritten characters. Finally, DLQDF, MCDNN and DeepCNet are chosen as baseline methods, and image sample sets of the operation tickets in operation and maintenance are used for algorithm verification. The result shows that CBTR can accurately recognize the images and texts in operation tickets and has performance superiority.