Neural networks in recognition of handwriting

Bernadetta Michalik

bernadetta768@gmail.com
Lublin University of Technology (Poland)

Marek Miłosz


Lublin University of Technology (Poland)

Abstract

Artificial neural networks consist of many simple elements capable of processing data. These are tools inspired by the construction of the human brain, used in machine learning. The aim of the research was to analyze the occuracy of the created neural network in the process of handwriting recognition. The article presents the results obtained during the learning and testing of a convolution network with a different number of hidden layers. Each time learning and testing the network was carried out using the same set of images (taken from the publicly available IAM database) depicting handwritten words in English.


Keywords:

handwriting; artificial neural network; word recognition

R. Tadeusiewicz, M. Szaleniec, Leksykon sieci neuronowych. Wydawnictwo Fundacji "Projekt Nauka" (2015).
  Google Scholar

K. Różanowski, Sztuczna inteligencja rozwój, szanse i zagrożenia. Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki (2007).
  Google Scholar

R. Mithe, S. Indalkar, N. Divekar, Optical character recognition. International journal of recent technology and engineering (IJRTE), 2 (2013) 72-75.
  Google Scholar

H. Li, Z. Lin, X. Shen, J. Brandt, G. Hua, A convolutional neural network cascade for face detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, (2015) 5325-5334.
DOI: https://doi.org/10.1109/CVPR.2015.7299170   Google Scholar

Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature, 521 (2015) 436-444.
DOI: https://doi.org/10.1038/nature14539   Google Scholar

M. Liang, H. Hu, Recurrent convolutional neural network for object recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, (2015) 3367-3375.
  Google Scholar

Y. Ma, Z. Xiang, Q. Du, W. Fan, Effects of user-provided photos on hotel review helpfulness: An analytical approach with deep leaning. International Journal of Hospitality Management, 71 (2018) 120-131.
DOI: https://doi.org/10.1016/j.ijhm.2017.12.008   Google Scholar

Y. Hou, H. Zhao, Handwritten digit recognition based on depth neural network. International Conference on Intelligent Informatics and Biomedical Sciences, Okinawa, 2017.
DOI: https://doi.org/10.1109/ICIIBMS.2017.8279710   Google Scholar

S. Albawi, T. A. Mohammed, S. Al-Zawi, Understanding of a convolutional neural network. In 2017 International Conference on Engineering and Technology (ICET), (2017) 1-6 .
DOI: https://doi.org/10.1109/ICEngTechnol.2017.8308186   Google Scholar

T. N. Sainath, A. R. Mohamed, B. Kingsbury, B. Ramabhadran, Deep convolutional neural networks for LVCSR. In 2013 IEEE international conference on acoustics, speech and signal processing, (2013) 8614-8618.
DOI: https://doi.org/10.1109/ICASSP.2013.6639347   Google Scholar

http://www.fki.inf.unibe.ch/databases/iam-handwriting-database [05.02.2019]
  Google Scholar

Download


Published
2020-06-30

Cited by

Michalik, B. ., & Miłosz, M. (2020). Neural networks in recognition of handwriting . Journal of Computer Sciences Institute, 15, 109–113. https://doi.org/10.35784/jcsi.2037

Authors

Bernadetta Michalik 
bernadetta768@gmail.com
Lublin University of Technology Poland

Authors

Marek Miłosz 

Lublin University of Technology Poland

Statistics

Abstract views: 396
PDF downloads: 497