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Optična razpoznava notnih znakov
Isovski, Matic (Author), Šajn, Luka (Mentor) More about this mentor... This link opens in a new window

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Abstract
V diplomskem delu sta predstavljena dva pristopa optičnega razpoznavanja notnih znakov: tradicionalni, pri katerem se problem rešuje po strategiji "deli in vladaj", ter novejši, holistični pristop, pri katerem se problem rešuje z globoko nevronsko mrežo. Oba sta podrobneje opisana in primerjana, predstavljeni pa sta tudi njuni implementaciji, optimizaciji ter doseženi rezultati. Opisani sta zbirki notnih incipitov PrIMuS in CorPus, s katerima sta bila modela ocenjena. Predstavljena sta tudi dva različna obstoječa sistema iz tega področja (plačljivi ter odprtokodni). Na koncu so primerjani še rezultati modelov, nastalih v sklopu diplomskega dela, ter rezultati obstoječih sistemov. Najboljše rezultate je dosegla metoda z globoko nevronsko mrežo, optimizirana tradicionalna metoda pa bi bila uporabna pri natančno definirani ali pa omejeni problematiki.

Language:Slovenian
Keywords:Optična razpoznava notnih znakov, računalniški vid, predprocesiranje slike, CRNN
Work type:Bachelor thesis/paper (mb11)
Tipology:2.11 - Undergraduate Thesis
Organization:FRI - Faculty of computer and information science
Year:2021
COBISS.SI-ID:75930371 This link opens in a new window
Views:61
Downloads:7
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Secondary language

Language:English
Title:Optical music notations recognition
Abstract:
The thesis presents two approaches to optical music recognition: the traditional one, in which the problem is solved by the strategy of "divide and rule", and the newer, holistic approach, in which the problem is solved with a deep neural network. Both are described and compared in more detail, and their implementation, optimization and achieved results are also presented. The collections of music incipits PrIMuS and CorPus, with which the models were evaluated, are described. Two different accommodation systems in this field (paid and open source) are also presented. Finally, the results of the models created as part of the diploma thesis and the results of existing systems are compared. The best results were achieved by the method with a deep neural network, while the optimized traditional method could be used with precisely defined or limited problem.

Keywords:Optical music recognition, computer vision, image pre-processing, CRNN

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