A Deep Transfer Learning Approach to Document Image Quality Assessment

Research output: Chapter in Book/Report/Conference proceedingConference paper

Abstract

Document image quality assessment (DIQA) is an important process for various applications such as optical character recognition (OCR) and document restoration. In this paper we propose a no-reference DIQA model based on a deep convolutional neural network (DCNN), where the rich knowledge of natural scene image characterization of a previously-trained DCNN is exploited towards OCR accuracy oriented document image quality assessment. Following a two-stage deep transfer learning procedure, we fine-tune the knowledge base of the DCNN in the first phase and bring in a task-specific segment consisting of three fully connected (FC) layers in the second phase. Based on the fine-tuned knowledge base, the task-specific segment is trained from scratch to facilitate the application of the transferred knowledge on the new task of document quality assessment. Testing results on a benchmark dataset demonstrate that the proposed model achieves state-of-the-art performance.
Original languageEnglish
Title of host publication2019 International Conference on Document Analysis and Recognition (ICDAR)
PublisherIEEE
Pages1372-1377
Number of pages6
ISBN (Print)978-1-7281-3014-9, 978-1-7281-3015-6
DOIs
Publication statusPublished - 3 Feb 2020
Event15th International Conference on Document Analysis and Recognition - International Convention Centre Sydney, Sydney, Australia
Duration: 20 Sep 201925 Sep 2019
https://icdar2019.org/

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
ISSN (Print)1520-5363

Conference

Conference15th International Conference on Document Analysis and Recognition
Abbreviated titleICDAR2019
Country/TerritoryAustralia
CitySydney
Period20/09/1925/09/19
Internet address

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This research is supported by the Auditing Digitisation Outputs in the Cultural Heritage Sector (ADOCHS) project (Contract No. BR/154/A6/ADOCHS), financed by the Belgian Science Policy (Belspo) within the scope of the BRAIN programme.

Publisher Copyright:
© 2019 IEEE.

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