Skip to main content
SpringerLink
Log in

Application of computer image processing in office automation system

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

With the development of science and technology, a variety of office automation systems (OAS) has been extensively applied in various occasions. Moreover, digital image processing technology has made great progress. The emergence of a series of excellent algorithms represented by Adaboost human face detection algorithm extends the application space of digital image processing in daily work and study. Besides, the operational capability of existing personal computers enables them to run smoothly these algorithms, which further contributes to the technological maturity of the digital image processing associated office automation systems. To keep up with the pace of information technology, this study selects high definition (HD) technology for paper archives in OAS, which is related to digital image processing as the research content. Automatic high definition demonstration of paper archives can reduce the burden on staff. This paper solved the problems of correction of slanted document image, automatic extraction of identification photo and color enhancement of seal and verified the feasibility of the scheme.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Cakmak, A.F., Benk, S., and Budak, T., The acceptance of tax office automation system (VEDOP) by employees: Factorial validation of Turkish adapted Technology Acceptance Model (TAM), Int. J. Econ. Finance, 2011, vol. 3, no. 6, pp. 107–116.

    Article  Google Scholar 

  2. Do, Q.B., Beghdadi, A., Luong, M., et al., A perceptual pyramidal watermarking technique, 2008 IEEE International Conference on Multimedia and Expo, 2008, pp. 281–284.

    Chapter  Google Scholar 

  3. Yang, J.C., Hou, C.P., Shen, L.L., et al., Objective evaluation method for stereo image quality based on PSNR, J. Tianjin Univ., 2008, vol. 41, no. 12, pp. 1448–1452.

    Google Scholar 

  4. Shen, H.Y., Sun, S.F., Wang, J.P., et al., Comparison of image quality objective evaluation, CiSE 2009. International Conference on Computational Intelligence and Software Engineering, 2009.

    Google Scholar 

  5. Suzuki, K. and Sakamoto, Y., Measurement method for objective evaluation of reconstructed image quality in CGH, SPIE OPTO, Int. Soc. Opt. Photonics, 2013, pp. 5419–5428.

    Google Scholar 

  6. Haonan, T. and Sumei, L., Objective evaluation method for image quality based on edge structure similarity, Acta Photonica Sin., 2013, vol. 42, no. 1, pp. 110–114.

    Article  Google Scholar 

  7. Rehman, A. and Saba, T., Document skew estimation and correction: Analysis of techniques, common problems and possible solutions, Appl. Artif. Intell., 2011, vol. 25, no. 9, pp. 769–787.

    Article  Google Scholar 

  8. Singh, C., Bhatia, N., and Kaur, A., Hough transform based fast skew detection and accurate skew correction methods, Pattern Recognit., 2008, vol. 25, no. 12, pp. 3528–3546.

    Article  MATH  Google Scholar 

  9. Saragiotis, P. and Papamarkos, N., Local skew correction in documents, Int. J. Pattern Recognit. Artif. Intell., 2008, vol. 22, no. 4, pp. 691–710.

    Article  Google Scholar 

  10. Al, S.A.M. and Khairuddin, O., Skew detection and correction technique for Arabic document images based on center of gravity, J. Comput. Sci., 2009, vol. 5, no. 5, pp. 363–368.

    Article  Google Scholar 

  11. Ichikawa, K., Mita, T., Hori, O., et al., Component-based face detection method for various types of occluded faces, ISCCSP 2008. 3rd International Symposium on Communications, Control and Signal Processing, 2008, pp. 538–543.

    Chapter  Google Scholar 

  12. Ito, Y., Ogawa, K., and Nakano, K., Fast ellipse detection algorithm using Hough transform on the GPU, 2013 International Conference on Computing, Networking and Communications (ICNC), 2013, pp. 313–319.

    Google Scholar 

  13. Yanjie, L., Rifei, L., Weibin, R., et al., A fast center detecting method based on improved randomized hough transform, Nanotechnol. Precis. Eng., 2011, vol. 9, no. 4, pp. 298–304.

    Google Scholar 

  14. Lienhart, R. and Maydt, J., An extended set of Haar-like features for rapid object detection, IEEE ICIP, 2002, no. 1, pp. 900–903.

    Google Scholar 

  15. Manigandan, M. and Jackin, I.M., Wireless vision based mobile robot control using hand gesture recognition through perceptual color space, International Conference on Advances in Computer Engineering, 2010, pp. 95–99.

    Google Scholar 

  16. Asmussen, S. and Rojas-Nandayapa, L., Asymptotics of sums of lognormal random variables with Gaussian copula, Stat. Probab. Lett., 2008, vol. 78, no. 16, pp. 2709–2714.

    Article  MathSciNet  MATH  Google Scholar 

  17. Ward, G., Hastie, T., Barry, S., et al., Presence-only data and the EM algorithm, Biometrics, 2009, vol. 65, no. 2, pp. 554–563.

    Article  MathSciNet  MATH  Google Scholar 

  18. Nakano, A., Okuda, H., Suzuki, T., et al., Symbolic modeling of driving behavior based on hierarchical segmentation and formal grammar, Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009, pp. 5516–5521.

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Inner Mongolia Electronic Information College, No. 69, Wulanchabu Road, Saihan District, Hohhot, Inner Mongolia, 010050, China

    Meizhi Zhang

Authors
  1. Meizhi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Meizhi Zhang.

Additional information

The article is published in the original.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, M. Application of computer image processing in office automation system. Aut. Control Comp. Sci. 50, 179–186 (2016). https://doi.org/10.3103/S0146411616030081

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411616030081

Keywords

Search

Navigation