EFFICIENCY COMPARISON OF NETWORKS IN HANDWRITTEN LATIN CHARACTERS RECOGNITION WITH DIACRITICS
Edyta ŁUKASIK
e.lukasik@pollub.plLublin University of Technology (Poland)
https://orcid.org/0000-0003-3644-9769
Wiktor FLIS
Lublin University of Technology (Poland)
https://orcid.org/0009-0002-6804-6630
Abstract
The aim of the article is to analyze and compare the performance and accuracy of architectures with a different number of parameters on the example of a set of handwritten Latin characters from the Polish Handwritten Characters Database (PHCD). It is a database of handwriting scans containing letters of the Latin alphabet as well as diacritics characteristic of the Polish language. Each class in the PHCD dataset contains 6,000 scans for each character. The research was carried out on six proposed architectures and compared with the architecture from the literature. Each of the models was trained for 50 epochs, and then the accuracy of prediction was measured on a separate test set. The experiment thus constructed was repeated 20 times for each model. Accuracy, number of parameters and number of floating-point operations performed by the network were compared. The research was conducted on subsets such as uppercase letters, lowercase letters, lowercase letters with diacritics, and a subset of all available characters. The relationship between the number of parameters and the accuracy of the model was indicated. Among the examined architectures, those that significantly improved the prediction accuracy at the expense of a larger network size were selected, and a network with a similar prediction accuracy as the base one, but with twice as many model parameters was selected.
Keywords:
convolutional neural network, model efficiency, handwritten text recognitionReferences
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Authors
Edyta ŁUKASIKe.lukasik@pollub.pl
Lublin University of Technology Poland
https://orcid.org/0000-0003-3644-9769
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