Predspracovanie obrazu pre optické rozpoznávanie ... - TUKE

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FEI TU v Košiciach Diplomová práca List č. 88 Obr. 49: Screenshot from the Image PreProcessing application. foreground. I created a ’Printed text simulator’ which generates images with text which is texturised with the textures found in the scanned documents. Examples of the generated pictures are on picture 50. The first experiment was intended to determine which combination of foreground and background on the generated image is the most problematic. As the graph on picture 51 shows that combination number 17 is the most problematic, so it was chosen for the next set of experiments on generated images. The next experiments showed that the neural network must have the input layer of size at least3 ×3 neurons to work sufficiently on the generated image and the hidden layer of size at least 3 neurons. I was also testing how the initialization of neural network affects the learning of neural network and also the final achieved minimal error of the neural network. I’ve found out that the average minimal error on the 17th combination of foreground and background was 7.82% and that it may vary about 1% because of different initialization. The other experiment showed that the neural network can process well the image with 15% or less of noise. I have done also experiments on the scanned image with the scanning
FEI TU v Košiciach Diplomová práca List č. 89 Obr. 50: Example of some foreground and background combinations error [%] 10 8 6 4 2 training pattern 0 0 5 10 15 20 25 foreground and background combination Obr. 51: Graph of relation between combination of textures and the minimal error reached on the neural network.
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Technická univerzita v Košiciach
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Poďakovanie Chcem poďakovať môj
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Thesis title: Image preprocessing f
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Obsah Formulácia úlohy 1 1 Úvod
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FEI TU v Košiciach Diplomová prá
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Predspracovanie obrazu pre optické rozpoznávanie ... - TUKE

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