Investigation and implementation of image super-resolution using CNNs applied to OCR
- Recent developments in the field of deep learning have shown promising advances for a wide range of historically difficult computer vision problems. Using advanced deep learning techniques, researchers manage to perform high-quality single-image super-resolution, i.e., increasing the resolution of a given image without major losses in image quality, usually encountered when using traditional approaches such as standard interpolation. This thesis examines the process of deep learning super-resolution using convolutional neural networks and investigates whether the same deep learning models can be used to increase OCR results for low-quality text images.
Author: | Florian WernerORCiD |
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URN: | urn:nbn:de:bsz:960-opus4-23425 |
DOI: | https://doi.org/10.25968/opus-2342 |
Advisor: | Adrian PigorsGND, Ralf BrunsORCiDGND |
Document Type: | Bachelor Thesis |
Language: | English |
Year of Completion: | 2022 |
Publishing Institution: | Hochschule Hannover |
Granting Institution: | Hochschule Hannover, Fakultät IV - Wirtschaft und Informatik |
Date of final exam: | 2022/08/15 |
Release Date: | 2022/09/05 |
Tag: | Super Resolution |
GND Keyword: | Deep learning; Neuronales Netz; Maschinelles Sehen; Optische Zeichenerkennung |
Page Number: | 92 |
Link to catalogue: | 1818711729 |
Institutes: | Fakultät IV - Wirtschaft und Informatik |
DDC classes: | 004 Informatik |
Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |