Skip to main content
SpringerLink
Log in

A new robust approach for altered handwritten text detection

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Altering handwritten documents is receiving special attention from researchers because it is useful in several sensitive crime applications such as the identification of suicide notes, fraudulent certificates, fake answer scripts, bank and property documents etc. This paper aims at developing a robust method for detecting altered text in handwritten document images in noisy and blurry environments. This study considers ten classes of handwritten text affected by multiple forgery operations with noise and blur as follows: (i) Normal-original text, (ii) Copy-paste forgery, (iii) Insertion forgery, (iv) Copy-paste and insertion forgery, (v) Noisy text, (vi) Blurred text, (vii) Copy-paste forgery with noise, (viii) Copy-paste forgery with blur, (ix) Insertion forgery with noise and (x) Insertion with blur. For ten-class classification, the proposed work explores the combination of statistical, gradient and texture features with a Bayesian classifier. The proposed approach works based on the premise that altered content in noisy and blurry handwritten documents exhibits inconsistent patterns of pixel arrangements while the original text exhibits a regular pattern of pixel arrangements. Comprehensive experiments on our dataset of 10-class and three standard datasets, namely, a dataset of forged handwritten text, a dataset of altered receipt images, and a dataset of forged IMEI number images are conducted to show effectiveness and robustness of the proposed approach compared to the state-of-the-art methods.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

The dataset and code is available at https://github.com/Gayatri-patilbujare/Altered-Handwritten-Text-Detection.

References

  1. Artaud C, Sidère N, Doucet A, Ogier J, Yooz VPD (2018) Find it! Fraud detection contest report. 2018 24th International Conference on Pattern Recognition (ICPR), pp 13–18. https://doi.org/10.1109/ICPR.2018.8545428

  2. Bouibed ML, Nemmour H, Chibani Y (2021) SVM-based writer retrieval system in handwritten document images. Multimed Tools Appl 81:22629–22651. https://doi.org/10.1007/s11042-020-10162-7

    Article  Google Scholar 

  3. Chen Y, Gao S (2020) Forgery numeral handwriting detection based on convolution neural network. (2020) IEEE 5th information technology and mechatronics engineering conference (ITOEC). https://doi.org/10.1109/itoec49072.2020.91418

  4. Cruz F, Sidere N, Coustaty M, D’Andecy VP, Ogier J-M (2017) Local binary patterns for document forgery detection. 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), pp 1223–1228. https://doi.org/10.1109/icdar.2017.202

  5. D’Amiano L, Cozzolino D, Poggi G, Verdoliva L (2018) A PatchMatch-based dense-field algorithm for video copy-move detection and localization. IEEE Trans Circuits Syst Video Technol 29:669–682. https://doi.org/10.1109/tcsvt.2018.2804768

    Article  Google Scholar 

  6. Dua S, Singh J, Parthasarathy H (2020) Image forgery detection based on statistical features of block DCT coefficients. Procedia Comput Sci 171:369–378. https://doi.org/10.1016/j.procs.2020.04.038

    Article  Google Scholar 

  7. Gaikwad A (2018) Analysis of copy move image forgery detection using histogram of orientated gradient. Int J Res Eng Appl Manag. https://doi.org/10.18231/2454-9150.2018.0687

  8. Gornale S, Dongare P, Manza R (2016) Detection of osteoarthritis using knee X-Ray image analyses: a machine vision based approach. Int J Comput Appl 145:20–26. https://doi.org/10.5120/ijca2016910544

    Article  Google Scholar 

  9. Gornale S, Dongare P, Marathe K, Hiremath P (2017) Determination of osteoarthritis using histogram of oriented gradients and multiclass SVM. Int J Image Graph Signal Process 9:41–49. https://doi.org/10.5815/ijigsp.2017.12.05

    Article  Google Scholar 

  10. Gornale S, Babaleshwar A, Yannawar P (2018) Detection and classification of signage’s from random mobile videos using local binary patterns. Int J Image Graph Signal Process 10:52–59. https://doi.org/10.5815/ijigsp.2018.02.06

    Article  Google Scholar 

  11. Khan RA, Lone SA (2020) A comprehensive study of document security system, open issues and challenges. Multimed Tools Appl 80:7039–7061. https://doi.org/10.1007/s11042-020-10061-x

    Article  Google Scholar 

  12. Khan Z, Shafait F, Mian A (2015) Automatic inks mismatch detection for forensic document analysis. Pattern Recognit 48:3615–3626. https://doi.org/10.1016/j.patcog.2015.04.008

    Article  Google Scholar 

  13. Khan MJ, Yousaf A, Khurshid K, Abbas A, Shafait F (2018) Automated forgery detection in multispectral document images using fuzzy clustering. 13th IAPR International Workshop on Document Analysis Systems (DAS), pp 393–398. https://doi.org/10.1109/das.2018.26

  14. Krishnani D, Shivakumara P, Lu T, Pal U, Lopresti D, Kumar GH (2021) A new context-based feature for classification of emotions in photographs. Multimed Tools Appl 80:15589–15618. https://doi.org/10.1007/s11042-020-10404-8

    Article  Google Scholar 

  15. Kundu S, Shivakumara P, Grouver A, Pal U, Lu T, Blumenstein M (2019) A new forged handwriting detection method based on Fourier spectral density and variation. In: Proc. Asian Conference on Pattern Recognition (ACPR), pp 136–150. https://doi.org/10.1007/978-3-030-41404-7_10

  16. Luo Z, Shafait F, Mian A (2015) Localized forgery detection in hyperspectral document images. 13th international conference on document analysis and recognition (ICDAR), pp 496–500. https://doi.org/10.1109/icdar.2015.7333811

  17. Mallika R (2017) Fraud detection using supervised learning algorithms. Int J Adv Res Comput Commun Eng. https://doi.org/10.17148/IJARCCE.2017.6602

  18. Mushtaq S, Mir AH (2014) Forgery detection using statistical features. Innovative applications of Computational Intelligence on Power, Energy and Controls with Their Impact on Humanity (CIPECH), pp 92–97. https://doi.org/10.1109/cipech.2014.7019062

  19. Nandanwar L, Shivakumara P, Pal U, Lu T, Lopresti D, Seraogi B, Chaudhuri BB (2020) A new method for detecting altered text in document images. In: Proc. ICPRAI, pp 93–108. https://doi.org/10.1007/978-3-030-59830-3_8

  20. Nandanwar L, Shivakumara P, Kanchan S, Basavaraja V, Guru DS, Pal U, Blumenstein M (2020) DCT-phase statistics for forged IMEI numbers and air ticket detection. Expert Syst Appl 164:114014. https://doi.org/10.1016/j.eswa.2020.114014

    Article  Google Scholar 

  21. Nandanwar L, Shivakumara P, Kundu S, Pal U, Lu T, Lopresti D (2021) Chebyshev-harmonic-Fourier-moments and deep CNNs for detecting forged handwriting. 25th international conference on pattern recognition (ICPR). https://doi.org/10.1109/icpr48806.2021.9412179

  22. Raghunandan KS, Shivakumara P, Navya BJ, Pooja G, Prakash N, Kumar GH, Pal U, Lu T (2016) Fourier coefficients for fraud handwritten document classification through age analysis. 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp 25–30. https://doi.org/10.1109/icfhr.2016.0018

  23. Revathy GS, Mathew D (2015) Region duplication forgery detection using histogram of oriented gradients. Int J Eng Res Technol. https://doi.org/10.17577/IJERTV4IS060501

  24. Sarma B, Nandi G (2014) A study on digital image forgery detection. Int J Adv Res Comput Sci Softw Eng 4(11):878–882

    Google Scholar 

  25. Shivakumara P, Basavaraja V, Gowda HS, Guru DS, Pal U, Lu T (2018) A new RGB based fusion for forged IMEI number detection in Mobile images. 2018 16th international conference on Frontiers in handwriting recognition (ICFHR), pp 386–391. https://doi.org/10.1109/icfhr-2018.2018.00074

Download references

Acknowledgements

The authors thank Mr. Lokesh, University of Malaya for sharing the code of existing methods and datasets to conduct comparative study experiments in this work.

The author of this work received partial support from Faculty Grant (GPF096A-2020, GPF096B-2020 and GPF096C-2020), University of Malaya, Malaysia.

Author information

Authors and Affiliations

  1. Department of Computer Science, Rani Channamma University, Belagavi, India

    Gayatri Patil & Shivanand S. Gornale

  2. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Palaiahnakote Shivakumara

  3. Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India

    Umapada Pal

  4. Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia

    Michael Blumenstein

Authors
  1. Gayatri Patil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Palaiahnakote Shivakumara
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shivanand S. Gornale
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Umapada Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michael Blumenstein
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gayatri Patil.

Ethics declarations

Conflict of interest

The authors are aware of this submission and have no conflicts of Interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Patil, G., Shivakumara, P., Gornale, S.S. et al. A new robust approach for altered handwritten text detection. Multimed Tools Appl 82, 20925–20949 (2023). https://doi.org/10.1007/s11042-022-14242-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-14242-8

Keywords

Search

Navigation