TYPED DIGITS RECOGNITION USING SEQUENTIAL PROBABILITY RATIO TEST
Journal Title: Вісник Національного Авіаційного Університету - Year 2018, Vol 74, Issue 1
Abstract
Purpose: the represented research results are aimed to better understanding of computer vision methods and their capabilities. Both the statistical classifier and an artificial neural network allows processing of typical objects with simple descriptors. Methods: considered methods are grounded at probabilistic theory, optimization theory, kernel density estima tion and computer-based simulation as a verification tool. Results: the considered artificial neural network a rchitecture for digits recognition has advantage in comparison with statistical method due to its better classification ability. Presented results of experimental verification prove that advantage in both single observation and sequential observation scenarios. Discussion: the approach can be implemented in a variety of computer vision systems that observe typed text in difficult noisy conditions.
Authors and Affiliations
Volodymyr Kharchenko, Iurii Chyrka
Keywords
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- EP ID EP502687
- DOI 10.18372/2306-1472.74.12286
- Views 119
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