Abstract
Recognising images using computers is a traditionally hard problem in computing, and one that becomes particularly difficult when these images are from the real world due to the large variations in them. This paper investigates the problem of recognising digits and characters in natural images using a deep neural network approach. The experiments explore the utilisation of a recently introduced dropout method which reduces overfitting. A number of different configuration networks are trained. It is found that the majority of networks give better accuracy when trained using the dropout method. This indicates that dropout is an effective method to improve training of deep neural networks on the application of recognising natural images of digits and characters.
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Barrow, E., Jayne, C., Eastwood, M. (2015). Deep Dropout Artificial Neural Networks for Recognising Digits and Characters in Natural Images. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_4
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DOI: https://doi.org/10.1007/978-3-319-26561-2_4
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