Skip to main content
SpringerLink
Log in

An effective DeepWINet CNN model for off-line text-independent writer identification

  • Theoretical Advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

Writer identification based on handwriting recognition is considered one of the most common research areas in pattern recognition and biometrics. It has attracted much attention in recent decades due to the urgent need to develop biometric systems for many security applications. In this paper, Deep Writer Identification Network (DeepWINet), an effective deep Convolutional Neural Network (CNN) for writer identification, is proposed. The proposed model is evaluated in two different ways. In the first scenario, DeepWINet’s CNN activation features, computed from the connected components of the writing, are passed to a customized nearest neighbor classifier for writer identification. In the second scenario, DeepWINet is evaluated as an end-to-end CNN network where the predicted results are averaged using an efficient strategy, Score Averaging Component-Decision Combiner. The proposed approach achieves competitive or the highest State-Of-The-Art performance on eight challenging handwritten databases with different languages.

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

Similar content being viewed by others

Data availability statement

Data sharing are not applicable to this manuscript as no datasets were generated or analyzed during the current study.

Notes

  1. Code: https://github.com/githubharald/WordSegmentation.

References

  1. Yapıcı MM, Tekerek A, Topaloğlu N (2021) Deep learning-based data augmentation method and signature verification system for offline handwritten signature. Pattern Anal Appl 24(1):165–179

    Article  Google Scholar 

  2. Porwik P, Doroz R, Orczyk T (2015) The k-nn classifier and self-adaptive hotelling data reduction technique in handwritten signatures recognition. Pattern Anal Appl 18(4):983–1001

    Article  MathSciNet  Google Scholar 

  3. Fornés A, Lladós J, Sánchez G, Bunke H (2008) Writer identification in old handwritten music scores. In: 2008 The eighth IAPR international workshop on document analysis systems, pp 347–353, Sept 2008

  4. Chahi A, Elmerabet Y, Ruichek Y, Touahni R (2020) Local gradient full-scale transform patterns based off-line text-independent writer identification. Appl Soft Comput 92:106277

    Article  Google Scholar 

  5. Al-Maadeed S, Hassaine A, Bouridane A, Tahir MA (2016) Novel geometric features for off-line writer identification. Pattern Anal Appl 19(3):699–708

    Article  MathSciNet  Google Scholar 

  6. Chahi A, Elkhadiri I, Elmerabet Y, Ruichek Y, Touahni R (2018) Block wise local binary count for off-line text-independent writer identification. Exp Syst Appl 93(Supplement C):1–14

    Article  Google Scholar 

  7. Arabadjis D, Giannopoulos F, Papaodysseus C, Zannos S, Rousopoulos P, Panagopoulos M, Blackwell C (2013) New mathematical and algorithmic schemes for pattern classification with application to the identification of writers of important ancient documents. Pattern Recogn 46(8):2278–2296

    Article  MATH  Google Scholar 

  8. Sahare P, Dhok SB (2017) Script identification algorithms: a survey. Int J Multimedia Inf Retrieval 6(3):211–232

    Article  Google Scholar 

  9. Liwicki M, Schlapbach A, Bunke H, Bengio S, Mariéthoz J, Richiardi J (2006) Writer identification for smart meeting room systems, pp 186–195. Springer , Heidelberg

  10. Siddiqi I, Djeddi C, Raza A, Souici-Meslati L (2015) Automatic analysis of handwriting for gender classification. Pattern Anal Appl 18(4):887–899

    Article  MathSciNet  Google Scholar 

  11. Franke K, Köppen M (2001) A computer-based system to support forensic studies on handwritten documents. Int J Document Anal Recogn 3(4):218–231

    Article  Google Scholar 

  12. Helli B, Moghaddam ME (2014) An off-line cheque handwritten forgery detection based on feature route density matrix. Pattern Anal Appl 17(4):747–762

    Article  MathSciNet  Google Scholar 

  13. Seuret M, Nicolaou A, Maier A, Christlein V, Stutzmann D (2020) Icfhr 2020 competition on image retrieval for historical handwritten fragments. In: 2020 17th International conference on frontiers in handwriting recognition (ICFHR), pp 216–221. IEEE

  14. Mehri M, Gomez-Krämer P, Héroux P, Boucher A, Mullot R (2017) A texture-based pixel labeling approach for historical books. Pattern Anal Appl 20(2):325–364

    Article  MathSciNet  Google Scholar 

  15. Chahi A, Elmerabet Y, Ruichek Y, Touahni R (2019) An effective and conceptually simple feature representation for off-line text-independent writer identification. Exp Syst Appl 123:357–376

    Article  Google Scholar 

  16. Chahi A, Elmerabet Y, Ruichek Y, Touahni R (2020) Cross multi-scale locally encoded gradient patterns for off-line text-independent writer identification. Eng Appl Artif Intell 89:103459

    Article  Google Scholar 

  17. Keglevic M, Fiel S, Sablatnig R (2018) Learning features for writer retrieval and identification using triplet cnns. In: 2018 16th International conference on frontiers in handwriting recognition (ICFHR), pp 211–216. IEEE

  18. Abdi MN, Khemakhem M (2015) A model-based approach to offline text-independent arabic writer identification and verification. Pattern Recogn 48(5):1890–1903

    Article  Google Scholar 

  19. Rehman A, Naz S, Razzak MI (2019) Writer identification using machine learning approaches: a comprehensive review. Multimedia Tools Appl 78(8):10889–10931

    Article  Google Scholar 

  20. Dargan S, Kumar M (2019) Writer identification system for indic and non-indic scripts: state-of-the-art survey. Arch Comput Methods Eng 26(4):1283–1311

    Article  Google Scholar 

  21. Chawki D, Labiba SM (2010) A texture based approach for arabic writer identification and verification. In: 2010 International conference on Machine and Web Intelligence (ICMWI), pp 115–120. IEEE

  22. Said HES, Tan TN, Baker KD (2000) Personal identification based on handwriting. Pattern Recogn 33(1):149–160

    Article  Google Scholar 

  23. Bertolini D, Oliveira LS, Justino E, Sabourin R (2013) Texture-based descriptors for writer identification and verification. Exp Syst Appl 40(6):2069–2080

    Article  Google Scholar 

  24. Nicolaou A, Bagdanov AD, Liwicki M, Karatzas D (2015) Sparse radial sampling lbp for writer identification. In: 2015 13th International conference on document analysis and recognition (ICDAR), pp 716–720. IEEE

  25. Singh P, Roy PP, Raman B (2018) Writer identification using texture features: a comparative study. Comput Electrical Eng 71:1–12

    Article  Google Scholar 

  26. He S, Schomaker L (2017) Writer identification using curvature-free features. Pattern Recogn 63:451–464

    Article  Google Scholar 

  27. Siddiqi I, Vincent N (2010) Text independent writer recognition using redundant writing patterns with contour-based orientation and curvature features. Pattern Recogn 43(11):3853–3865

    Article  MATH  Google Scholar 

  28. Brink A, Smit J, Bulacu M, Schomaker L (2012) Writer identification using directional ink-trace width measurements. Pattern Recogn 45(1):162–171

    Article  Google Scholar 

  29. He S, Schomaker L (2014) Delta-n hinge: rotation-invariant features for writer identification. In: 2014 22nd International conference on pattern recognition, pp 2023–2028. IEEE

  30. Khalifa E, Al-Maadeed S, Tahir MA, Bouridane A, Jamshed A (2015) Off-line writer identification using an ensemble of grapheme codebook features. Pattern Recogn Lett 59:18–25

    Article  Google Scholar 

  31. He S, Wiering M, Schomaker L (2015) Junction detection in handwritten documents and its application to writer identification. Pattern Recogn 48(12):4036–4048

    Article  Google Scholar 

  32. He S, Samara P, Burgers J, Schomaker L (2016) Image-based historical manuscript dating using contour and stroke fragments. Pattern Recogn 58:159–171

    Article  Google Scholar 

  33. Mohammed H, Mäergner V, Konidaris T, Stiehl HS (2017) Normalised local naïve bayes nearest-neighbour classifier for offline writer identification. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR), vol 1, pp 1013–1018. IEEE

  34. Fiel S, Sablatnig R (2013) Writer identification and writer retrieval using the fisher vector on visual vocabularies. In: 2013 12th International conference on document analysis and recognition, pp 545–549, Aug 2013

  35. Christlein Vincent, Bernecker David, Hönig Florian, Maier Andreas, Angelopoulou Elli (2017) Writer identification using gmm supervectors and exemplar-svms. Pattern Recogn 63:258–267

    Article  Google Scholar 

  36. Khan FA, Khelifi F, Tahir MA, Bouridane A (2019) Dissimilarity gaussian mixture models for efficient offline handwritten text-independent identification using sift and rootsift descriptors. IEEE Trans Inf Forensics Security 14(2):289–303

    Article  Google Scholar 

  37. Fiel S, Sablatnig R (2015) Writer identification and retrieval using a convolutional neural network. In: International conference on computer analysis of images and patterns, pp 26–37. Springer

  38. Christlein V, Bernecker D, Maier A, Angelopoulou E (2015) Offline writer identification using convolutional neural network activation features. In: German conference on pattern recognition, pp 540–552. Springer

  39. Christlein V, Gropp M, Fiel S, Maier A (2017) Unsupervised feature learning for writer identification and writer retrieval. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR), vol 1, pp 991–997. IEEE

  40. Xing L, Qiao Y (2016) Deepwriter: A multi-stream deep cnn for text-independent writer identification. In: 2016 15th International conference on frontiers in handwriting recognition (ICFHR), pp 584–589. IEEE

  41. Christlein V, Maier A (2018) Encoding cnn activations for writer recognition. In: 2018 13th IAPR international workshop on document analysis systems (DAS), pp 169–174. IEEE

  42. Chen S, Wang Y, Lin C-T, Ding W, Cao Z (2019) Semi-supervised feature learning for improving writer identification. Inf Sci 482:156–170

    Article  MathSciNet  Google Scholar 

  43. Manmatha R, Srimal N (1999) Scale space technique for word segmentation in handwritten documents. In: International conference on scale-space theories in computer vision, pp 22–33. Springer

  44. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition

  45. Marti U-V, Bunke H (2002) The iam-database: an english sentence database for offline handwriting recognition. Int J Document Anal Recogn 5(1):39–46

    Article  MATH  Google Scholar 

  46. Pechwitz M, Maddouri SS, Märgner V, Ellouze N, Amiri H (2002) Ifn/enit - database of handwritten arabic words. In Proceedings of the CIFED 2002, pp 129–136

  47. Louloudis G, Gatos B, Stamatopoulos N, Papandreou A (2013) Icdar 2013 competition on writer identification. In: 2013 12th International conference on document analysis and recognition, pp 1397–1401. IEEE

  48. Schomaker L, Vuurpijl L (2000) Forensic writer identification: a benchmark data set and a comparison of two systems [internal report for the Netherlands Forensic Institute]. Technical report, Nijmegen: NICI

  49. Kleber F, Diem SM, Sablatnig R (2013) Cvl-database: An off-line database for writer retrieval, writer identification and word spotting. In: 2013 12th International conference on document analysis and recognition, pp 560–564, Aug 2013

  50. He S, Schomaker L (2020) Fragnet: writer identification using deep fragment networks. IEEE Trans Inf Forensics Security 15:3013–3022

    Article  Google Scholar 

  51. Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980

  52. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  MATH  Google Scholar 

  53. Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650

    Article  MathSciNet  MATH  Google Scholar 

  54. Ojansivu V, Heikkilä J (2008) Blur insensitive texture classification using local phase quantization, pp 236–243. Springer, Berlin

  55. Yaacoub HI, Mohamed El YEK (2016) Writer identification using texture descriptors of handwritten fragments. Exp Syst Appl 47:14–22

  56. Khan FA, Tahir MA, Khelifi F, Bouridane A (2016) Offline text independent writer identification using ensemble of multi-scale local ternary pattern histograms. In: 2016 6th European workshop on visual information processing (EUVIP), pp 1–6, Oct 2016

  57. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst, pp 1097–1105

  58. Kumar P, Sharma A (2019) Dcwi: distribution descriptive curve and cellular automata based writer identification. Exp Syst Appl 128:187–200

    Article  Google Scholar 

  59. Hadjadji B, Chibani Y (2018) Two combination stages of clustered one-class classifiers for writer identification from text fragments. Pattern Recogn 82:147–162

    Article  Google Scholar 

  60. Kumar P, Sharma A (2020) Segmentation-free writer identification based on convolutional neural network. Comput Electrical Eng 85:106707

    Article  Google Scholar 

  61. Kessentini Yousri, BenAbderrahim Sana, Djeddi Chawki (2018) Evidential combination of svm classifiers for writer recognition. Neurocomputing 313:1–13

    Article  Google Scholar 

  62. Javidi Malihe, Jampour Mahdi (2020) A deep learning framework for text-independent writer identification. Eng Appl Artif Intell 95:103912

    Article  Google Scholar 

  63. Fiel S, Sablatnig R (2015) Writer identification and retrieval using a convolutional neural network, pp 26–37. Springer International Publishing, Cham

  64. He S, Schomaker L (2021) Gr-rnn: Global-context residual recurrent neural networks for writer identification. Pattern Recogn 117:107975

    Article  Google Scholar 

  65. Tang Y, Wu X (2016) Text-independent writer identification via cnn features and joint bayesian. In: 2016 15th international conference on frontiers in handwriting recognition (ICFHR), pp 566–571. IEEE

  66. Christlein V, Bernecker D, Hönig F, Angelopoulou E (2014) Writer identification and verification using gmm supervectors. In: IEEE winter conference on applications of computer vision, pp 998–1005. IEEE

  67. Nguyen HT, Nguyen CT, Ino T, Indurkhya B, Nakagawa M (2019) Text-independent writer identification using convolutional neural network. Pattern Recogn Lett 121:104–112

    Article  Google Scholar 

  68. Durou A, Aref I, Al-Maadeed S, Bouridane A, Benkhelifa E (2019) Writer identification approach based on bag of words with obi features. Inf Process Manage 56(2):354–366

    Article  Google Scholar 

  69. Litifu A, Yan Y, Xiao J, Jiang H (2021) Writer identification using redundant writing patterns and dual-factor analysis of variance. Appl Intell , pp 1–16

Download references

Author information

Authors and Affiliations

  1. CIAD UMR 7533, Univ. Bourgogne Franche-Comté, UTBM, 25200, Montbéliard, France

    Abderrazak Chahi & Yassine Ruichek

  2. Laboratoire LASTID, Département de Physique, Faculté des Sciences, Université Ibn Tofail, BP 133, 14000, Kenitra, Morocco

    Abderrazak Chahi, Youssef El-merabet & Raja Touahni

Authors
  1. Abderrazak Chahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Youssef El-merabet
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yassine Ruichek
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Raja Touahni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abderrazak Chahi.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare that this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere.

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

Chahi, A., El-merabet, Y., Ruichek, Y. et al. An effective DeepWINet CNN model for off-line text-independent writer identification. Pattern Anal Applic 26, 1539–1556 (2023). https://doi.org/10.1007/s10044-023-01186-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-023-01186-4

Keywords

Search

Navigation