Publication: A Smart Visual Sensing Concept Involvin...

Master data
Title: A Smart Visual Sensing Concept Involving Deep Learning for a Robust Optical Character Recognition under Hard Real-World Conditions
Subtitle:
Abstract: 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.
Keywords: Electrical and Electronic Engineering, Biochemistry, Instrumentation, Atomic and Molecular Physics, and Optics, Analytical Chemistry
Publication type: Article in journal (Authorship)
Publication date: 12.08.2022 (Online)
Published by: Sensors
Sensors
to publication
 ( MDPI Publishing; )
Title of the series: -
Volume number: 22
Issue: 16
First publication: Yes
Version: -
Page: -
Total number of pages: 6025 pp.

Versionen
Keine Version vorhanden
Publication date: 12.08.2022
ISBN (e-book): -
eISSN: 1424-8220
DOI: http://dx.doi.org/10.3390/s22166025
Homepage: -
Open access
  • Available online (open access)

Assignment
Organisation Address
Fakultät für Technische Wissenschaften
  Institut für Intelligente Systemtechnologien
Universitätsstraße 65-67
9020 Klagenfurt am Wörthersee
Austria
   hubert.zangl@aau.at
http://www.uni-klu.ac.at/tewi/ict/sst/index.html
To organisation
Universitätsstraße 65-67
AT - 9020  Klagenfurt am Wörthersee

Categorisation
Subject areas
  • 102003 - Image processing
  • 102018 - Artificial neural networks
  • 102019 - Machine learning
Research Cluster
  • Self-organizing systems
  • Humans in the Digital Age
Citation index
  • Science Citation Index Expanded (SCI Expanded)
Information about the citation index: Master Journal List
Peer reviewed
  • Yes
Publication focus
  • Science to Science (Quality indicator: I)
Classification raster of the assigned organisational units:
working groups
  • Transportation Informatics Group

Cooperations
No partner organisations selected

Articles of the publication
No related publications