Skip to main content
SpringerLink
Log in

ATP-DenseNet: a hybrid deep learning-based gender identification of handwriting

  • S.I. : Higher Level Artificial Neural Network Based Intelligent Systems
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Digital forensics has a vital effect in several domains and mainly focuses on reactive measures, especially when facing digital incidents. Gender identification becomes the important problem in the realm of forensic techniques and handwriting recognition. In this paper, attention-based two-pathway Densely Connected Convolutional Networks (ATP-DenseNet) is proposed to identify the gender of handwriting. There are two pathways in ATP-DenseNet: Feature pyramid could extract hierarchical page feature, and attention-based DenseNet (A-DenseNet) could extract the word feature by fusing Convolutional Block Attention Module (CBAM) and dense connected block. Finally, ATP-DenseNet makes the final prediction combining the two pathways. Experimental results show the efficiency of ATP-DenseNet, and the proposed method performs better than other researches. And the visualization of the feature maps can help us to know which part of the image contributes most to the gender identity.

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

Similar content being viewed by others

Notes

  1. http://tc11.cvc.uab.es/datasets/GenderIdentifify2013_1/gt_1_1.

  2. http://www.iam.unibe.ch/fki.

  3. http://khatt.ideas2serve.net.

References

  1. Van Beek HMA, van Eijk EJ, van Baar RB, Ugen M, Bodde JNC, Siemelink AJ (2015) Digital forensics as a service: game on. Digit. Investig 15:20–38. https://doi.org/10.1016/j.diin.2015.07.004

    Article  Google Scholar 

  2. Ellison D, Venter H (2016) An ontology for digital security and digital forensics investigative techniques. In: Proceedings of the 11th international conference on cyber warfare and security, ICCWS, pp. 119–127

  3. Al-Masri E, Bai Y, Li J (2018) A fog-based digital forensics investigation framework for IoT systems. In: 2018 IEEE international conference on smart cloud (SmartCloud), pp. 196–201, IEEE

  4. Shen MX, Wu ZX, Park TS (2012) Securing evidence of file wiping for digital forensic on flash memory. J KIISE: Databases 39(5):271–278

    Google Scholar 

  5. Akremi A, Sallay H, Rouached M, Bouaziz R (2020) Applying digital forensics to service oriented architecture. Int J Web Serv Res (IJWSR) 17(1):17–42. https://doi.org/10.4018/IJWSR.2020010102

    Article  Google Scholar 

  6. Dargan S (2018) Writer identification system for indic and non-indic scripts: state-of-the-art survey. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-018-9278-z

    Article  Google Scholar 

  7. Bi N, Suen CY, Nobile N, Tan J (2019) A multi-feature selection approach for gender identification of handwriting based on kernel mutual information. Pattern Recogn Lett 121:123–132. https://doi.org/10.1016/j.patrec.2018.05.005

    Article  Google Scholar 

  8. Tett RP, Palmer CA (1997) The validity of handwriting elements in relation to self-report personality trait measures. Personal Individ Differ 22:11–18. https://doi.org/10.1016/S0191-8869(96)00183-3

    Article  Google Scholar 

  9. Beech JR, Mackintosh IC (2005) Do differences in sex hormones affect handwriting style? evidence from digit ratio and sex role identity as determinants of the sex of handwriting. Pers Individ Differ 39:459–468

    Article  Google Scholar 

  10. Bulacu M, Schomaker L (2007) Text-independent writer identification and verification using textural and allographic features. IEEE Trans Pattern Anal Mach Intell 29(4):701–717. https://doi.org/10.1109/TPAMI.2007.1009

    Article  Google Scholar 

  11. Du L, You X, Xu H, Gao Z, Tang Y (2010) Wavelet domain local binary pattern features for writer identification. In: Proceedings of 20th international conference on pattern recognition (ICPR), IEEE. pp. 3691–3694

  12. Tan T (1992) Texture feature extraction via visual cortical channel modelling. In: Proceedings of 11th IAPR international conference on pattern recognition Vol. III. conference c: image, speech and signal analysis, IEEE. pp. 607–610

  13. Liu CL (2007) Normalization-cooperated gradient feature extraction for handwritten character recognition. IEEE Trans Pattern Anal Mach Intell 29:1465–1469

    Article  Google Scholar 

  14. Tan J, Lai JH, Zuo XX. (2012) The dataset system of economic dispute handwritten (dsedh) based on stroke shape and structure features. In: Proceedings of 21st international conference on pattern recognition (ICPR), IEEE. pp. 661–664

  15. Xu L, Ding X, Peng L, Li X (2011) An improved method based on weighted grid micro-structure feature for text-independent writer recognition. In: Proceedings of international conference on document analysis and recognition (ICDAR), IEEE. pp. 638–642

  16. Wen J, Fang B, Chen J, Tang Y, Chen H (2012) Fragmented edge structure coding for chinese writer identification. Neurocomputing 86:45–51

    Article  Google Scholar 

  17. Newell AJ, Griffin LD (2014) Writer identification using oriented basic image features and the delta encoding. Pattern Recognit. 47(6):2255–2265. https://doi.org/10.1016/j.patcog.2013.11.029

    Article  Google Scholar 

  18. Said H, Tan T, Baker K (2000) Personal identification based on handwriting. microprocessors 33(1):149–160. https://doi.org/10.1016/S0031-3203(99)00006-0

    Article  Google Scholar 

  19. Chayan H, Md. Obaidullah Sk., Kaushik R (2016) Offline writer identification from isolated characters using textural features. In: Proceedings of the 4th international conference on frontiers in intelligent computing: theory and applications (FICTA) 2015. Springer India. http://dx.doi.org/10.1007/978-81-322-2695-6_20

  20. Helli B, Moghaddam ME (2010) A text-independent persian writer identification based on feature relation graph (frg). Pattern Recogn 43(6):2199–2209. https://doi.org/10.1016/j.patcog.2009.11.026

    Article  Google Scholar 

  21. H. Yaâcoub, I. Siddiqi, & M. E. Y. E. Kettani, 2015. Arabic writer identification using local binary patterns (lbp) of handwritten fragments. http://dx.doi.org/10.1007/978-3-319-19390-8_27

  22. Arandjelovic R, Zisserman A (2012) Three things everyone should know to improve object retrieval. In: Computer vision and pattern recognition. IEEE. http://dx.doi.org/10.1109/CVPR.2012.6248018

  23. Ghiasi G, Safabakhsh R (2013) Offline text-independent writer identification using codebook and efficient code extraction methods. Image Vis Comput 31(5):379–391. https://doi.org/10.1016/j.imavis.2013.03.002

    Article  Google Scholar 

  24. Brink AA, Smit J, Bulacu ML, Schomaker LRB (2012) Writer identification using directional ink-trace width measurements. Pattern Recogn 45(1):162–171. https://doi.org/10.1016/j.patcog.2011.07.005

    Article  Google Scholar 

  25. Siddiqi I, Vincent N (2010) Text independent writer recognition using redundant writing patterns with contour-based orientation and curvature features. Pattern Recognit 43(11):3853–3865. https://doi.org/10.1016/j.patcog.2010.05.019

    Article  MATH  Google Scholar 

  26. Ahmadian A, Fouladi K, Araabi BN (2016) Writer identification using a probabilistic model of handwritten digits and approximate bayesian computation. In: International conference of signal processing and intelligent systems (ICSPIS), IEEE. http://dx.doi.org/10.1109/ICSPIS.2016.7869875

  27. Abdi MN, Khemakhem M (2015) A model-based approach to offline text-independent arabic writer identification and verification. Pattern Recognit 48(5):1890–1903. https://doi.org/10.1016/j.patcog.2014.10.027

    Article  Google Scholar 

  28. Schomaker L, Wiering M, He S (2015) Junction detection in handwritten documents and its application to writer identification. Pattern Recognit 48(12):4036–4048. https://doi.org/10.1016/j.patcog.2015.05.022

    Article  Google Scholar 

  29. Schomaker L, Bulacu M (2004) Automatic writer identification using connected-component contours and edge-based features of uppercase western script. IEEE Trans Pattern Anal Mach Intell 26(6):787–798. https://doi.org/10.1109/tpami.2004.18

    Article  Google Scholar 

  30. Wu YB, Lu HZ, Zhang ZY (2017) Text-independent online writer identification using hidden markov models. IEICE Trans Inf Syst E100.D(2):332–339. https://doi.org/10.1587/transinf.2016EDP7238

    Article  Google Scholar 

  31. V. Christlein, M. Gropp, S. Fiel, and A. Maier, 2017. Unsupervised feature learning for writer identification and writer retrieval. In: Proc. 14th IAPR Int. Conf. Document Anal. Recognit. (ICDAR), vol. 1, pp. 991–997. https://doi.org/10.1109/icdar.2017.165

  32. Stefan F, Robert S (2015) Writer identification and retrieval using a convolutional neural network. Computer analysis of images and patterns. Springer, Berlin. https://doi.org/10.1007/978-3-319-23117-4_3

    Book  Google Scholar 

  33. Liu M, Jin L, Xie Z (2017) PS-LSTM: capturing essential sequential online information with path signature and LSTM for writer identification. In: IAPR international conference on document analysis & recognition. IEEE Computer Society. http://dx.doi.org/10.1109/ICDAR.2017.114

  34. Yang W, Jin L, Liu M (2015) Chinese character-level writer identification using path signature feature, DropStroke and deep CNN. In: 13th international conference on document analysis and recognition (ICDAR). IEEE. https://doi.org/10.1109/icdar.2015.7333821

  35. Sheng H, Schomaker L (2020) Fragnet: writer identification using deep fragment networks. IEEE Trans Inf Forensics Secur 15:99. https://doi.org/10.1109/TIFS.2020.2981236

    Article  Google Scholar 

  36. 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. http://dx.doi.org/10.1109/ICFHR.2016.0112

  37. Zhang XY, Xie GS, Liu CL, Bengio Y (2016) End-to-end online writer identification with recurrent neural network. IEEE Trans Hum-Mach Syst 47(2):285–292. https://doi.org/10.1109/THMS.2016.2634921

    Article  Google Scholar 

  38. Woo S, Park J, Lee J.-Y, Kweon IS (2018) CBAM: convolutional block attention module. In: Proc. Eur. Conf. Comput. Vis. (ECCV), Munich, German, Sep. 2018, pp. 8–14

  39. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, 2017, pp. 2261–2269

  40. Lin TY, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2016) Feature pyramid networks for object detection

  41. Zagoruyko S, Komodakis N (2017) Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer. In: ICLR

  42. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proc. IEEE conf. comput. vis. pattern recognit.(CVPR), Jun. 2016, pp. 770–778

  43. D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” in Proc. Int. Conf. Learn. Represent. (ICLR), 2015

  44. Hassaine A, Maadeed SA, Aljaam J, Jaoua A (2013) ICDAR 2013 competition on gender prediction from handwriting. In: International conference on document analysis & recognition. IEEE Computer Society

  45. Maadeed SA, Ayouby W, Hassaine A, Aljaam JM (2012) QUWI: an Arabic and English handwriting dataset for offline writer identification. In: International conference on frontiers in handwriting recognition (ICFHR), IEEE, 2012, pp. 746–751

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

    Article  Google Scholar 

  47. Mahmoud SA, Ahmad I, AI-Khatib WG, Alshayeb M, Parvez MT, Margner V, Fink GA (2014) KHATT: an open arabic offline handwritten text database. Pattern Recognit 47(3):1096–1112

    Article  Google Scholar 

  48. Abbasi H, Olyaee M, Ghafari HR (2013) Rectifying reverse polygonization of digital curves for dominant point detection. Int J Comput Sci Issues (IJCSI) 10:154–163

    Google Scholar 

  49. Blumenstein M, Liu XY, Verma B (2007) An investigation of the modified direction feature for cursive character recognition. Pattern Recogn 40:376–388

    Article  Google Scholar 

  50. Siddiqi, I., Vincent, N., 2009. A set of chain code based features for writer recognition. In: Proceedings of 10th international conference on document analysis and recognition(ICDAR), IEEE. pp. 981–985

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

    Article  Google Scholar 

  52. Ibrahim AS, Youssef AE, Abbott AL (2014) Global vs local features for gender identification using arabic and english handwriting. In: Proc. ISSPIT, pp 155–160, 2014

  53. N. Bouadjenek, H. Nemmour and Y. Chibani, “Local descriptors to improve offline handwriting-based gender identification”, In Proc. ICSCP, pp 43-46, 2014

  54. Bouadjenek N, Nemmour H, Chibani Y (2015) Age, gender and handedness prediction from handwriting using gradient features. In: Proc. ICDAR, pp 1116–1120, 2015

  55. Navya BJ, Shivakumara P, Shwetha GC, Roy S, Lu T (2018) Adaptive multi-gradient kernels for handwritting based gender identification. In: International conference on frontiers in handwriting recognition

  56. Brahmachary TK, Ahmed S, Mia MS (2018) Safety and quality management practices in construction sector: a case study. J Syst Manag Sci 8(2):47–64

    Google Scholar 

  57. Hai Li, Fan Chunxiao Wu, Yuexin Liu Jie, Lilin Rao (2014) Research of LDAP-based IOT object information management scheme. J Logist Inf Serv Sci 1(1):51–60

    Google Scholar 

  58. Zhao PX, Gao WQ, Han X, Luo WH (2019) Bi-objective collaborative scheduling optimization of airport ferry vehicle and tractor. Int J Simul Modell 18(2):355–365. https://doi.org/10.2507/IJSIMM18(2)CO9

    Article  Google Scholar 

  59. Xu W, Yin Y (2018) Functional objectives decision-making of discrete manufacturing system based on integrated ant colony optimization and particle swarm optimization approach. Adv Prod Eng Manag 13(4):389–404. https://doi.org/10.14743/apem2018.4.298

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by Beijing Social Science Foundation Grant 19JDGLA002, Foundation for Distinguished Young Talents in Higher Education of Henan (CN) (Grant No. 19YJC630043), the National Natural Science Foundation (Grant No. J1824031). We appreciate their support very much.

Funding

This work was funded by Beijing Social Science Foundation Grant 19JDGLA002.

Author information

Authors and Affiliations

  1. School of Economics and Management, Beijing Jiaotong University, Beijing, 100044, China

    Gang Xue, Shifeng Liu, Daqing Gong & Yicao Ma

Authors
  1. Gang Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shifeng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daqing Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yicao Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

DG and SL contributed to conceptualization; DG and GX helped with methodology; GX contributed to software; and GX and YM helped with writing—original draft preparation.

Corresponding author

Correspondence to Daqing Gong.

Ethics declarations

Conflict of interest

The authors declare no conflict 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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xue, G., Liu, S., Gong, D. et al. ATP-DenseNet: a hybrid deep learning-based gender identification of handwriting. Neural Comput & Applic 33, 4611–4622 (2021). https://doi.org/10.1007/s00521-020-05237-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05237-3

Keywords

Search

Navigation