Publikation: Deep Neural Network Concept for a Blind...
Stammdaten
Titel: | Deep Neural Network Concept for a Blind Enhancement of Document-Images in the Presence of Multiple Distortions |
Untertitel: | |
Kurzfassung: | In this paper, we propose a new convolutional neural network (CNN) architecture for improving document-image quality through decreasing the impact of distortions (i.e., blur, shadows, contrast issues, and noise) contained therein. Indeed, for many document-image processing systems such as OCR (optical character recognition) and document-image classification, the real-world image distortions can significantly degrade the performance of such systems in a way such that they become merely unusable. Therefore, a robust document-image enhancement model is required to preprocess the involved document images. The preprocessor system developed in this paper places “deblurring” and “noise removal and contrast enhancement” in two separate and sequential submodules. In the architecture of those two submodules, three new parts are introduced: (a) the patch-based approach, (b) preprocessing layer involving Gabor and Blur filters, and (c) the approach using residual blocks. Using these last-listed innovations results in a very promising performance when compared to the related works. Indeed, it is demonstrated that even extremely strongly degraded document images that were not previously recognizable by an OCR system can now become well-recognized with a 91.51% character recognition accuracy after the image enhancement preprocessing through our new CNN model. |
Schlagworte: | convolutional neural network; blind image enhancement; blur enhancement; contrast enhancement; noise reduction |
Publikationstyp: | Beitrag in Zeitschrift (Autorenschaft) |
Erscheinungsdatum: | 24.09.2022 (Online) |
Erschienen in: |
Applied Sciences
Applied Sciences
(
MDPI Publishing;
T. Kobayashi
)
zur Publikation |
Titel der Serie: | - |
Bandnummer: | 12 |
Heftnummer: | 19 |
Erstveröffentlichung: | Ja |
Version: | - |
Seite: | - |
Gesamtseitenanzahl: | 9601 S. |
Versionen
Keine Version vorhanden |
Erscheinungsdatum: | 24.09.2022 |
ISBN (e-book): | - |
eISSN: | 2076-3417 |
DOI: | http://dx.doi.org/10.3390/app12199601 |
Homepage: | - |
Open Access |
|
Zuordnung
Organisation | Adresse | ||||
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Fakultät für Technische Wissenschaften
Institut für Intelligente Systemtechnologien
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AT - 9020 Klagenfurt am Wörthersee |
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Sachgebiete | |
Forschungscluster |
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