改进的基于深度学习算法的手写数字识别方法

R. Jantayev, Y. Amirgaliyev
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引用次数: 3

摘要

重要目标的识别与分类是计算机视觉的核心问题之一。虽然在图像处理的计算和精度性能方面做了大量的工作,但仍然受到模糊性的限制。在目前的工作中,我们比较了传统的机器学习方法和深度学习模型,即卷积神经网络(CNN),在使用MNIST数据集的手写数字识别上。我们发现CNN算法比支持向量机(SVM)的识别精度更高。
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Improved Handwritten Digit Recognition method using Deep Learning Algorithm
One of the essential problems in Computer Vision is identification and classification of important objects. While exhaustive work done on image processing for computation and accuracy performance it is still limited by ambiguity. In current work we compared traditional machine learning method versus Deep Learning model, namely Convolutional Neural Network(CNN), on Handwritten Digit Recognition using MNIST dataset. We showed that CNN algorithm reaches higher recognition accuracy than Support Vector Machine(SVM).
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