Thesis Details
Holistické rozpoznání registrační značky pomocí konvolučních neuronových sítí
The goal of this work is to create a model of neural network for holistic recognition of license plates, focused on accuracy and shortening of the learning process. The model was implemented as a union of convolutional neural network for extraction of deep features of a plate and Bidirectional LSTM with CTC. The trained model was compared to another implementation using a holistic approach, that was trained on the same dataset. My design of the network achieved better results in recognition on a dataset, which is different from the training one, with an error rate of 8.3 %.
convolutional neural networks, Bidirectional LSTM, CTC loss, Python, Keras, TensorFlow, image processing, license plate recognition, deep learning
Bařina David, Ing., Ph.D. (DCGM FIT BUT), člen
Burget Radek, doc. Ing., Ph.D. (DIFS FIT BUT), člen
Češka Milan, doc. RNDr., Ph.D. (DITS FIT BUT), člen
Mrázek Vojtěch, Ing., Ph.D. (DCSY FIT BUT), člen
@bachelorsthesis{FITBT24954,
author = "Du\v{s}an Morbitzer",
type = "Bachelor's thesis",
title = "Holistick\'{e} rozpozn\'{a}n\'{i} registra\v{c}n\'{i} zna\v{c}ky pomoc\'{i} konvolu\v{c}n\'{i}ch neuronov\'{y}ch s\'{i}t\'{i}",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2022,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/24954/"
}