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Camera Captured DIQA with Linearity and Monotonicity Constraints

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Document Analysis Systems (DAS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12116))

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Abstract

Document image quality assessment (DIQA), which predicts the visual quality of the document images, can not only be applied to estimate document’s optical character recognition (OCR) performance prior to any actual recognition, but also provides immediate feedback on whether the documents meet the quality requirements for other high level document processing and analysis tasks. In this work, we present a deep neural network (DNN) to accomplish the DIQA task, where a Saimese based deep convolutional neural network (DCNN) is employed with customized losses to improve system’s capability of linearity and monotonicity to predict the quality of document images. Based on the proposed network along with the new losses, the obtained DCNN achieves the state-of-the-art quality assessment performance on the public datasets. The source codes and pre-trained models are available at https://gitlab.com/xujun.peng/DIQA-linearity-monotonicity.

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Author information

Authors and Affiliations

  1. ISI, University of Southern California, Marina del Rey, CA, USA

    Xujun Peng

  2. LinkedMed Co., Ltd., Spreadtrum Center, Zuchongzhi Road, Shanghai, China

    Chao Wang

Authors
  1. Xujun Peng
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  2. Chao Wang
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Correspondence to Xujun Peng .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Xiang Bai

  2. Autonomous University of Barcelona, Barcelona, Spain

    Dimosthenis Karatzas

  3. Lehigh University, Bethlehem, PA, USA

    Daniel Lopresti

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Peng, X., Wang, C. (2020). Camera Captured DIQA with Linearity and Monotonicity Constraints. In: Bai, X., Karatzas, D., Lopresti, D. (eds) Document Analysis Systems. DAS 2020. Lecture Notes in Computer Science(), vol 12116. Springer, Cham. https://doi.org/10.1007/978-3-030-57058-3_13

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  • DOI: https://doi.org/10.1007/978-3-030-57058-3_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57057-6

  • Online ISBN: 978-3-030-57058-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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