Effectiveness of artificial neural networks in recognising handwriting characters

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Published: Sep 30, 2018
DOI: https://doi.org/10.35784/jcsi.680
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DOI

https://doi.org/10.35784/jcsi.680

Authors

Marek Miłosz

m.milosz@pollub.pl

Institute of Computer Science, Lublin University of Technology, Nadbystrzycka 36B, 20-618 Lublin, Poland, Poland
Janusz Gazda

janusz.gazda@pollub.edu.pl

Institute of Computer Science, Lublin University of Technology, Nadbystrzycka 36B, 20-618 Lublin, Poland, Poland

Abstract

Artificial neural networks are one of the tools of modern text recognising systems from images, including handwritten ones. The article presents the results of a computational experiment aimed at analyzing the quality of recognition of handwritten digits by two artificial neural networks (ANNs) with different architecture and parameters. The correctness indicator was used as the basic criterion for the quality of character recognition. In addition, the number of neurons and their layers and the ANNs learning time were analyzed. The Python language and the TensorFlow library were used to create the ANNs, and software for their learning and testing. Both ANNs were learned and tested using the same big sets of images of handwritten characters.

Keywords:

character recognition; handwriting; artificial neural networks

References

Article Details

Miłosz, M., & Gazda, . J. (2018). Effectiveness of artificial neural networks in recognising handwriting characters. Journal of Computer Sciences Institute, 7, 210–214. https://doi.org/10.35784/jcsi.680