TYPED DIGITS RECOGNITION USING SEQUENTIAL PROBABILITY RATIO TEST - Kharchenko, Volodymyr; Chyrka, Iurii (Proceedings of National Aviation University (archive), 2018)
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 estimation and computer-based simulation as a verification tool. Results: the considered artificial neural network architecture 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.