使用卷积神经网络的药物代码识别

A. Zaafouri, M. Sayadi
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引用次数: 0

摘要

本文提出了一种基于卷积神经网络(CNN)的医疗产品过期码自动识别方法。使用非锐化掩蔽方法增强输入图像。然后使用局部自适应阈值技术(LATT)对图像进行二值化,并使用形态学算子对图像进行稀疏化。利用边界框技术提取图像的特征。最后,煮沸一组字符(a - z)和数字(0-9)。字符数据集为CNN提供了一种采用的结构,以识别药物的过期代码。在各种复杂条件下的大型字符数据集上对该方法进行了测试。实验结果证明了该方法的鲁棒性。所开发的系统在字符识别上达到了约93%的准确率。
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Medication Code Recognition using Convolutional Neural Network
In this paper, a new automatic method for expiration code of medical products using convolutional neural network (CNN) is presented. The input image is enhanced using unsharp masking method. Then the image is binarized using local adaptive thresholding technique (LATT) and thinned using morphological operator. Also, characters of the image are extracted using bounding box technique. Finally, a set of characters (A-Z) and digits (0-9) is boiled. The dataset of characters feed an adopted architecture of CNN in order to recognize expiration code of the medication. The proposed approach is tested on large datasets of characters under various conditions of complexities. The experimental results demonstrate the robustness of our approach. The developed system achieves approximately 93% accuracy on character recognition.
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