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Label number Recognition Based on Convolutional Neural Networks in industrial products

机译：基于工业产品卷积神经网络的标签号识别

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摘要

Aiming at the identification of a certain kind of industrial black material product, this paper proposes a method based on convolutional neural network (CNN) for digital identification of product labels. The platform of image acquisition is set up first, then the digital region is segmented through image processing algorithm and data set is built on it. Finally, the visual geometry group (VGG16) model of convolutional neural network is used to realize the identification of digital labels. Compared with the nearest neighbor based on local binary patterns histograms (LBPH-NN) algorithm and the support vector machine (SVM) algorithm, the performance of CNN is better comprehensively. This research has a good practical significance in the field of industrial production.
机译：旨在识别某种工业黑色材料产品，本文提出了一种基于卷积神经网络（CNN）的方法，用于产品标签的数字识别。首先建立图像采集平台，然后通过图像处理算法分割数字区域，并构建数据集。最后，使用卷积神经网络的视觉几何组（VGG16）模型来实现数字标签的识别。与基于局部二进制图案直方图（LBPH-NN）算法和支持向量机（SVM）算法的最近邻居相比，CNN的性能更好地全面。该研究在工业生产领域具有良好的现实意义。

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