Neural Network for Handwriting Recognition

Authors

  • Mikhail M. Butaev JSC Research and Production Enterprise «Rubin», 440000, Russia
  • Mikhail Yu. Babich JSC Research and Production Enterprise «Rubin», 440000, Russia
  • Igor I. Salnikovq Department of Computational Machines and Systems, Penza State Technological University
  • Alexey I. Martyshkin Department of Computational Machines and Systems, Penza State Technological University, 440039, Russia
  • Dmitry V. Pashchenko Penza State Technological University
  • Dmitry A. Trokoz Penza State Technological University

DOI:

https://doi.org/10.5377/nexo.v33i02.10798

Keywords:

neural network, pattern recognition, neural network algorithms, accuracy, network training, network retraining

Abstract

Today, in the digital age, the problem of pattern recognition is very relevant. In particular, the task of text recognition is important in banking, for the automatic reading of documents and their control; in video control systems, for example, to identify the license plate of a car that violated traffic rules; in security systems, for example, to check banknotes at an ATM and in many other areas. A large number of methods are known for solving the problem of pattern recognition, but the main advantage of neural networks over other methods is their learning ability. It is this feature that makes neural networks attractive to study. The article proposes a basic neural network model. The main algorithms are considered and a programming model is implemented in the Python programming language. In the course of research, the following shortcomings of the basic model were revealed: low learning rate (the number of correctly recognized digits in the first epochs of learning); retraining - the network has not learned to generalize the knowledge gained; low probability of recognition - 95.13%.To solve the above disadvantages, various techniques were used that increase the accuracy and speed of work, as well as reduce the effect of network retraining.

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Published

2020-12-31

How to Cite

M. Butaev, M., Yu. Babich, M., Salnikovq, I. I., Martyshkin, A. I., Pashchenko, D. V., & Trokoz, D. A. (2020). Neural Network for Handwriting Recognition. Nexo Scientific Journal, 33(02), 623–637. https://doi.org/10.5377/nexo.v33i02.10798

Issue

Vol. 33 No. 02 (2020)

Section

Articles

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