Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Rank-Revealing Orthogonal Decomposition in Extreme Learning Machine Design Read chapter Read first chapter

2018 | OriginalPaper | Chapter

TextNet for Text-Related Image Quality Assessment

Authors : Hongyu Li, Junhua Qiu, Fan Zhu

Published in: Artificial Neural Networks and Machine Learning – ICANN 2018

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

With the rapid increase of consumer photos, annotating and retrieving such images with text are becoming more significant, which requires optical character recognition (OCR) techniques. However, to predict OCR accuracy, text-related image quality assessment (TIQA) is necessary and of great value, especially in online business processes. With more interests in text, TIQA aims to compute the quality score of an image through predicting the degree of degradation at textual regions.
To assess text-related quality on detected textlines, this paper proposes a deep neural network, TextNet, which mainly includes three layers: encoder, decoder, and prediction. The decoder layer combines the encoded feature map with the decoded map through deconvolution and concatenation. The prediction layer is designed for textline detection and quality assessment with a new loss function. Under the TIQA framework, the overall text-related image quality is computed through pooling the quality of all detected textlines by way of weighted averaging. Experimental results show that the proposed framework can work well in jointly assessing text related image quality and detecting textlines, even for unknown scene images.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Reinforcement Learning for Joint Extraction of Entities and Relations
next chapter A Target Dominant Sets Clustering Algorithm
Footnotes
Literature
1.
2.
Buta, M., Neumann, L., Matas, J.: FASText: efficient unconstrained scene text detector. In: IEEE International Conference on Computer Vision, pp. 1206–1214 (2015)
3.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.F.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255 (2009)
4.
Kang, L., Ye, P., Li, Y., Doermann, D.: A deep learning approach to document image quality assessment. In: IEEE International Conference on Image Processing, pp. 2570–2574 (2014)
5.
Karatzas, D., et al.: ICDAR 2015 competition on robust reading. In: International Conference on Document Analysis and Recognition, pp. 1156–1160 (2015)
6.
Karatzas, D., et al.: ICDAR 2013 robust reading competition. In: International Conference on Document Analysis and Recognition, pp. 1484–1493 (2013)
7.
Kim, K.H., Hong, S., Roh, B., Cheon, Y., Park, M.: PVANET: deep but lightweight neural networks for real-time object detection (2016)
8.
Kumar, J., Chen, F., Doermann, D.: Sharpness estimation for document and scene images. In: International Conference on Pattern Recognition, pp. 3292–3295 (2013)
9.
10.
Nayef, N.: Metric-based no-reference quality assessment of heterogeneous document images. In: SPIE Electronic Imaging, p. 94020L-12 (2015)
11.
Nayef, N., Luqman, M.M., Prum, S., Eskenazi, S., Chazalon, J., Ogier, J.M.: SmartDoc-QA: a dataset for quality assessment of smartphone captured document images - single and multiple distortions. In: International Conference on Document Analysis and Recognition, pp. 1231–1235 (2015)
12.
Peng, X., Cao, H., Natarajan, P.: Document image quality assessment using discriminative sparse representation. In: Document Analysis Systems, pp. 227–232 (2016)
13.
Rusinol, M., Chazalon, J., Ogier, J.M.: Combining focus measure operators to predict OCR accuracy in mobile-captured document images. In: IAPR International Workshop on Document Analysis Systems, pp. 181–185 (2014)
14.
Shi, B., Yao, C., Liao, M., Yang, M., Xu, P., Cui, L., Belongie, S., Lu, S., Bai, X.: ICDAR 2017 competition on reading Chinese text in the wild (RCTW-17). In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 01, pp. 1429–1434 (2017)
15.
Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Cardoso, M.J.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations (2017)
16.
Tian, Z., Huang, W., He, T., He, P., Qiao, Y.: Detecting text in natural image with connectionist text proposal network. In: European Conference on Computer Vision, pp. 56–72 (2016)CrossRef
17.
Xu, J., Ye, P., Li, Q., Liu, Y., Doermann, D.: No-reference document image quality assessment based on high order image statistics. In: IEEE International Conference on Image Processing, pp. 3289–3293 (2016)
18.
Ye, P., Doermann, D.: Document image quality assessment: a brief survey. In: International Conference on Document Analysis and Recognition, pp. 723–727 (2013)
19.
Ye, Q., Doermann, D.: Text detection and recognition in imagery: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 37(7), 1480–1500 (2015)CrossRef
20.
Zhang, Z., Zhang, C., Shen, W., Yao, C., Liu, W., Bai, X.: Multi-oriented text detection with fully convolutional networks, pp. 4159–4167 (2016)
21.
Zhou, X., et al.: EAST: an efficient and accurate scene text detector. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21–26 July 2017, pp. 2642–2651 (2017)
22.
Zhu, Y., Yao, C., Bai, X.: Scene text detection and recognition: recent advances and future trends. Front. Comput. Sci. 10(1), 19–36 (2016)CrossRef
Metadata
Title
TextNet for Text-Related Image Quality Assessment
Authors
Hongyu Li
Junhua Qiu
Fan Zhu
Copyright Year
2018
Book
Artificial Neural Networks and Machine Learning – ICANN 2018

Print ISBN: 978-3-030-01420-9

Electronic ISBN: 978-3-030-01421-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01421-6_27

Premium Partner

    Image Credits