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Natural Computing

15.12.2022

CA-NN: a cellular automata neural network for handwritten pattern recognition

verfasst von: Aamir Wali

Erschienen in: Natural Computing

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Abstract

Convolutional neural networks (CNNs) are best suited for image data. The most important layer in CNNs is the convolution layer. In this paper, cellular automata neural network or CA-NN is proposed for handwritten pattern recognition that replaces the convolution layer in CNN with the cellular automata (CA) layer. The idea is to make CNNs more biological where an image pattern would grow or decay instead of convolving. To grow or decay an image, CA is used as they are designed precisely for this purpose. In doing so, what would be the response of CA-NN to smaller data sets since CNNs require very large data to train. The model is tested and compared with 3 different well-known CNN architectures using 6 relatively small-sized handwritten data sets. The experimental results are very promising and raise very interesting future directions.

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Metadaten
Titel
CA-NN: a cellular automata neural network for handwritten pattern recognition
verfasst von
Aamir Wali
Publikationsdatum
15.12.2022
Verlag
Springer Netherlands
Erschienen in
Natural Computing
Print ISSN: 1567-7818
Elektronische ISSN: 1572-9796
DOI
https://doi.org/10.1007/s11047-022-09937-8

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