Publikation: A Smart Visual Sensing Concept Involvin...

Stammdaten
Titel: A Smart Visual Sensing Concept Involving Deep Learning for a Robust Optical Character Recognition under Hard Real-World Conditions
Untertitel:
Kurzfassung: In this study, we propose a new model for optical character recognition (OCR) based on both CNNs (convolutional neural networks) and RNNs (recurrent neural networks). The distortions affecting the document image can take different forms, such as blur (focus blur, motion blur, etc.), shadow, bad contrast, etc. Document-image distortions significantly decrease the performance of OCR systems, to the extent that they reach a performance close to zero. Therefore, a robust OCR model that performs robustly even under hard (distortion) conditions is still sorely needed. However, our comprehensive study in this paper shows that various related works can somewhat improve their respective OCR recognition performance of degraded document images (e.g., captured by smartphone cameras under different conditions and, thus, distorted by shadows, contrast, blur, etc.), but it is worth underscoring, that improved recognition is neither sufficient nor always satisfactory—especially in very harsh conditions. Therefore, in this paper, we suggest and develop a much better and fully different approach and model architecture, which significantly outperforms the aforementioned previous related works. Furthermore, a new dataset was gathered to show a series of different and well-representative real-world scenarios of hard distortion conditions. The new OCR model suggested performs in such a way that even document images (even from the hardest conditions) that were previously not recognizable by other OCR systems can be fully recognized with up to 97.5% accuracy/precision by our new deep-learning-based OCR model.
Schlagworte: Electrical and Electronic Engineering, Biochemistry, Instrumentation, Atomic and Molecular Physics, and Optics, Analytical Chemistry
Publikationstyp: Beitrag in Zeitschrift (Autorenschaft)
Erscheinungsdatum: 12.08.2022 (Online)
Erschienen in: Sensors
Sensors
zur Publikation
 ( MDPI Publishing; )
Titel der Serie: -
Bandnummer: 22
Heftnummer: 16
Erstveröffentlichung: Ja
Version: -
Seite: -
Gesamtseitenanzahl: 6025 S.

Versionen
Keine Version vorhanden
Erscheinungsdatum: 12.08.2022
ISBN (e-book): -
eISSN: 1424-8220
DOI: http://dx.doi.org/10.3390/s22166025
Homepage: -
Open Access
  • Online verfügbar (Open Access)

Zuordnung
Organisation Adresse
Fakultät für Technische Wissenschaften
  Institut für Intelligente Systemtechnologien
Universitätsstraße 65-67
9020 Klagenfurt am Wörthersee
Österreich
   hubert.zangl@aau.at
http://www.uni-klu.ac.at/tewi/ict/sst/index.html
zur Organisation
Universitätsstraße 65-67
AT - 9020  Klagenfurt am Wörthersee

Kategorisierung
Sachgebiete
  • 102003 - Bildverarbeitung
  • 102018 - Künstliche Neuronale Netze
  • 102019 - Machine Learning
Forschungscluster
  • Selbstorganisierende Systeme
  • Humans in the Digital Age
Zitationsindex
  • Science Citation Index Expanded (SCI Expanded)
Informationen zum Zitationsindex: Master Journal List
Peer Reviewed
  • Ja
Publikationsfokus
  • Science to Science (Qualitätsindikator: I)
Klassifikationsraster der zugeordneten Organisationseinheiten:
Arbeitsgruppen
  • Transportation Informatics Group

Kooperationen
Keine Partnerorganisation ausgewählt

Beiträge der Publikation
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