<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In this paper, the difficult problem of character recognition in natural scenes caused by many factors such as variability of light in the natural scene, background clutter and inaccurate viewing angle, and inconsistent resolution. Based on the deep learning framework PyTorch, a convolutional neural network is implemented. Based on the classic LeNet-5 network, the network optimizes the input layer to accept three-channel images, changes the pooling method to maximum pooling to simplify parameters, and the activation function is replaced by Rectified Linear Unit with faster convergence. The cross-entropy loss is used instead of the minimum mean square error to mitigate the slow learning. Furthermore, we also enroll the gradient descent optimization algorithm RMSprop and L2 regularization to improve the accuracy, speed up the convergence and suppress the over-fitting. The experiment results show that our model achieved an accuracy of 92.32% after training for 7h24min on the street view house number(SVHN) dataset, effectively improving the performance of LeNet-5.</p></abstract>

In this paper, the difficult problem of character recognition in natural scenes caused by many factors such as variability of light in the natural scene, background clutter and inaccurate viewing angle, and inconsistent resolution. Based on the deep learning framework PyTorch, a convolutional neural network is implemented. Based on the classic LeNet-5 network, the network optimizes the input layer to accept three-channel images, changes the pooling method to maximum pooling to simplify parameters, and the activation function is replaced by Rectified Linear Unit with faster convergence. The cross-entropy loss is used instead of the minimum mean square error to mitigate the slow learning. Furthermore, we also enroll the gradient descent optimization algorithm RMSprop and L2 regularization to improve the accuracy, speed up the convergence and suppress the over-fitting. The experiment results show that our model achieved an accuracy of 92.32% after training for 7h24min on the street view house number(SVHN) dataset, effectively improving the performance of LeNet-5.

Street View House Number Identification Based on Deep Learning

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Street View House Number Identification Based on Deep Learning"}

In this paper, the difficult problem of character recognition in natural scenes caused by many factors such as variability of light in the natural scene,...

Street View House Number Identification Based on Deep Learning

Data publikacji: 11 lis 2019

Zakres stron: 47 - 52

DOI: https://doi.org/10.21307/ijanmc-2019-058

Słowa kluczowe

© 2019 Haoqi Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.