A Deep Transfer Learning Approach to Document Image Quality Assessment

Research output: Unpublished contribution to conferencePoster

11 Citations (Scopus)

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
Pages1372-1377
Number of pages6
Publication statusPublished - 25 Sep 2019
Event15th International Conference on Document Analysis and Recognition - International Convention Centre Sydney, Sydney, Australia
Duration: 20 Sep 201925 Sep 2019
https://icdar2019.org/

Conference

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

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  • Towards Content Independent No-reference Image Quality Assessment Using Deep Learning

    Lu, T. & Dooms, A., Jul 2019, 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC). July-2019 ed. Xiamen, China: IEEE, Vol. July-2019. p. 276-280 5 p. 8981378. (Proceedings of 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC); no. July-2019).

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

    Open Access
    5 Citations (Scopus)

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