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Mobile Captured Glass Board Image Enhancement

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Computer Vision and Image Processing (CVIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1777))

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Abstract

Note-taking methods and devices have improved tremendously over the past few decades, and people are finding new ways to write notes and take photos. Automatic extraction, recognition, and retrieval are necessary to process the huge chunk of digitized document data. However, an important step in all of these pipelines is the pre-processing step, mainly image enhancement or clean-up, which enhances the text regions and suppresses the non-text regions. In this article, we look at the problem of image enhancement or clean-up on one such important class of images (i.e., mobile captured glass board images). We present a simple yet efficient algorithm using the concepts of classical image processing techniques to solve the problem, and the obtained results are promising in comparison to the Office Lens.

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Notes

  1. 1.

    https://play.google.com/store/apps/details?id=com.microsoft.office.officelens &hl=en_IN.

References

  1. Bataineh, B., Abdullah, S.N.H.S., Omar, K.: An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows. Pattern Recogn. Lett. 32(14), 1805–1813 (2011)

    Article  Google Scholar 

  2. Feng, M.L., Tan, Y.P.: Contrast adaptive binarization of low quality document images. IEICE Electron. Express 1(16), 501–506 (2004)

    Article  Google Scholar 

  3. Gatos, B., Pratikakis, I., Perantonis, S.J.: Adaptive degraded document image binarization. Pattern Recogn. 39(3), 317–327 (2006)

    Article  MATH  Google Scholar 

  4. Khurshid, K., Siddiqi, I., Faure, C., Vincent, N.: Comparison of niblack inspired binarization methods for ancient documents. In: Document Recognition and Retrieval XVI. 7247, pp. 267–275 (2009)

    Google Scholar 

  5. Koirala, P., Hauta-Kasari, M., Parkkinen, J.: Highlight removal from single image. In: International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 176–187 (2009)

    Google Scholar 

  6. Lazzara, G., Géraud, T.: Efficient multiscale sauvola’s binarization. Int. J. Doc. Anal. Recogn. (IJDAR) 17(2), 105–123 (2014)

    Article  Google Scholar 

  7. Niblack, W.: An Introduction to Digital Image Processing. Strandberg Publishing Company (1985)

    Google Scholar 

  8. Nurhadiyatna, A., Jatmiko, W., Hardjono, B., Wibisono, A., Sina, I., Mursanto, P.: Background subtraction using gaussian mixture model enhanced by hole filling algorithm (GMMHF). In: International Conference on Systems, Man, and Cybernetics, pp. 4006–4011 (2013)

    Google Scholar 

  9. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

  10. Sauvola, J., Seppanen, T., Haapakoski, S., Pietikainen, M.: Adaptive document binarization. In: International Conference on Document Analysis and Recognition (ICDAR). vol. 1, pp. 147–152 (1997)

    Google Scholar 

  11. Su, B., Lu, S., Tan, C.L.: Binarization of historical document images using the local maximum and minimum, pp. 159–166 (2010)

    Google Scholar 

  12. Su, B., Lu, S., Tan, C.L.: Robust document image binarization technique for degraded document images. IEEE Trans. Image Process. 22(4), 1408–1417 (2012)

    MathSciNet  MATH  Google Scholar 

  13. Wolf, C., Jolion, J.M.: Extraction and recognition of artificial text in multimedia documents. Formal Pattern Anal. Appl. 6(4), 309–326 (2004)

    MathSciNet  Google Scholar 

  14. Zhang, Z., He, L.w.: Note-taking with a camera: whiteboard scanning and image enhancement. In: IEEE International Conference on Acoustics, Speech, and Signal Processing. vol. 3, pp. 111–533 (2004)

    Google Scholar 

  15. Zhang, Z., He, L.W.: Whiteboard scanning and image enhancement. Digital Signal Process. 17(2), 414–432 (2007)

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Centre for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    Boddapati Mahesh, Ajoy Mondal & C. V. Jawahar

Authors
  1. Boddapati Mahesh
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  2. Ajoy Mondal
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  3. C. V. Jawahar
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Corresponding author

Correspondence to Ajoy Mondal .

Editor information

Editors and Affiliations

  1. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Deep Gupta

  2. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Kishor Bhurchandi

  3. Indian Institute of Technology Ropar, Rupnagar, India

    Subrahmanyam Murala

  4. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  5. Indian Institute of Technology Roorkee, Roorkee, India

    Sanjeev Kumar

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Mahesh, B., Mondal, A., Jawahar, C.V. (2023). Mobile Captured Glass Board Image Enhancement. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_18

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  • DOI: https://doi.org/10.1007/978-3-031-31417-9_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31416-2

  • Online ISBN: 978-3-031-31417-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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