Skip to main content
SpringerLink
Log in

Deep neural networks-based offline writer identification using heterogeneous handwriting data: an evaluation via a novel standard dataset

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

In modern societies, handwritten identification is a great practical need and challenge for forensic sciences. Moreover, few research studies have focused on automatic offline handwritten document analysis. Also, there are very few standard datasets on handwritten document identification. In addition, handwritten documents lose their nature over time due to the spread and drying of ink. From this standpoint, the present study presents an offline writer identification system (in presence of the uncertainties such as different experimental conditions and environmental noises for more realistic assumptions) that is a critical need in forensic studies. For this purpose, a comprehensive right-to-left dataset is developed and devised by gathering data from 62 participants at different time intervals under different experimental conditions. This dataset is designed based on American Society for Testing and Materials (ASTM) standards. A Deep Convolutional Neural Network (DCNN) model based on modified pre-trained networks is designed and developed to extract features from raw data hierarchically. The proposed DCNN model is tested and investigated not only on the designed dataset but also on different datasets. One notable advantage of the present study is that it has made use of heterogeneous data. Another remarkable aspect of the proposed study is that the proposed DCNN model is independent of any specific languages which can be applied on various languages. The results of the study indicate that the proposed DCNN model can learn features hierarchically from the handwriting raw data and achieve higher accuracy than other comparative 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
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  • Adak C, Chaudhuri BB, Blumenstein M (2019) An empirical study on writer identification and verification from intra-variable individual handwriting. IEEE Access 7:24738–24758

    Article  Google Scholar 

  • Ahmed AA, Hasan HR, Hameed FA, Al-Sanjary OI (2017) Writer identification on multi-script handwritten using optimum features. Kurd J Appl Res 2(3):178–185

    Article  Google Scholar 

  • Akiba T, Suzuki S, Fukuda K (2017) Extremely large minibatch sgd: Training resnet-50 on imagenet in 15 minutes. arXiv:1711.04325

  • Amin-Naji M, Aghagolzadeh A, Ezoji M (2020) CNNs hard voting for multi-focus image fusion. J Ambient Intell Human Comput 11:1749–1769. https://doi.org/10.1007/s12652-019-01199-0

  • Baghshah MS, Shouraki SB, Kasaei S (2006) A novel fuzzy classifier using fuzzy LVQ to recognize online Persian handwriting. In: 2006 2nd international conference on information & communication technologies, vol 1. IEEE, pp 1878–1883

  • Barratt S, Sharma R (2018) A note on the inception score. arXiv:1801.01973

  • Bennour A, Djeddi C, Gattal A, Siddiqi I, Mekhaznia T (2019) Handwriting based writer recognition using implicit shape codebook. Forensic Sci Int 301:91–100

    Article  Google Scholar 

  • Bottou L (2010) Large-scale machine learning with stochastic gradient descent. In: Proceedings of COMPSTAT'2010, Physica-Verlag HD, pp 177–186

  • 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

    Article  Google Scholar 

  • Burges C, Shaked T, Renshaw E, Lazier A, Deeds M, Hamilton N, Hullender G (2005) Learning to rank using gradient descent. In: Proceedings of the 22nd international conference on Machine learning, pp 89–96

  • Carbune V, Gonnet P, Deselaers T, Rowley HA, Daryin A, Calvo M, Wang L-L, Keysers D, Feuz S, Gervais P (2020) Fast multi-language LSTM-based online handwriting recognition. Int J Doc Anal Recogn (IJDAR) 23(2):89–102

  • Chahi A, Ruichek Y, Touahni R (2018) Block wise local binary count for off-line text-independent writer identification. Expert Syst Appl 93:1–14

    Article  Google Scholar 

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

  • Coleman C, Kang D, Narayanan D, Nardi L, Zhao T, Zhang J, Zaharia M (2019) Analysis of dawnbench, a time-to-accuracy machine learning performance benchmark. ACM SIGOPS Oper Syst Rev 53(1):14–25

    Article  Google Scholar 

  • Dang Y, Anand D, Sethi A (2019) Pixel-wise Segmentation of Right Ventricle of Heart. In: TENCON 2019–2019 IEEE Region 10 Conference (TENCON), IEEE, pp 1797–1802

  • Domingos P (2000) Bayesian averaging of classifiers and the overfitting problem. ICML 747:223–230

    Google Scholar 

  • DoubleApaper (2017) http://igepa-allcart.com/myuploads/WDAcjSnxTpKf2iBf1415718830.pdf

  • 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 Manag 56(2):354–366

    Article  Google Scholar 

  • E2290-07a (2007) A Standard Guide for examination of handwritten items, in ASTM International. West Conshohocken

  • Eltay M, Zidouri A, Ahmad I (2020) Exploring deep learning approaches to recognize handwritten arabic texts. IEEE Access 8:89882–89898

    Article  Google Scholar 

  • Faber-Castell (2017) http://www.faber-castell.in/40526/Products/Exports/default_news.aspx

  • Garg NK, Kumar M (2018) Writer identification system for handwritten Gurmukhi characters: Study of different feature-classifier combinations. In: Proceedings of international conference on computational intelligence and data engineering, Springer, Singapore, pp 125–131

  • Ghanim TM, Khalil MI, Abbas HM (2020) Comparative study on deep convolution neural networks DCNN-based offline Arabic handwriting recognition. IEEE Access 8:95465–95482

    Article  Google Scholar 

  • Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning, vol 1. MIT press, Cambridge

    MATH  Google Scholar 

  • Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Chen T (2018) Recent advances in convolutional neural networks. Pattern Recogn 77:354–377

    Article  Google Scholar 

  • Hafemann LG, Sabourin R, Oliveira LS (2019) Characterizing and evaluating adversarial examples for offline handwritten signature verification. IEEE Trans Inf Forensics Secur 14(8):2153–2166

    Article  Google Scholar 

  • Hagan MT, Demuth HB, Beale MH (1996) Neural network design. PWS, Boston

    Google Scholar 

  • Hannad Y, Siddiqi I, El Kettani MEY (2016) Writer identification using texture descriptors of handwritten fragments. Expert Syst Appl 47:14–22

    Article  Google Scholar 

  • Hannad Y, Siddiqi I, Djeddi C, El-Kettani MEY (2019) Improving Arabic writer identification using score-level fusion of textural descriptors. IET Biometrics 8(3):221–229

    Article  Google Scholar 

  • Hassan AR, Bhuiyan MIH (2016) Computer-aided sleep staging using complete ensemble empirical mode decomposition with adaptive noise and bootstrap aggregating. Biomed Signal Process Control 24:1–10

    Article  Google Scholar 

  • Hawkins DM (2004) The problem of overfitting. J Chem Inf Comput Sci 44(1):1–12

    Article  MathSciNet  Google Scholar 

  • He S, Schomaker L (2019) Deep adaptive learning for writer identification based on single handwritten word images. Pattern Recognit 88:64–74

    Article  Google Scholar 

  • Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580

  • Huang G, Liu S, Van der Maaten L, Weinberger KQ (2018) Condensenet: An efficient densenet using learned group convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2752–2761

  • Hung SL, Adeli H (1993) Parallel backpropagation learning algorithms on Cray Y-MP8/864 supercomputer. Neurocomputing 5(6):287–302

    Article  Google Scholar 

  • Iandola F, Moskewicz M, Karayev S, Girshick R, Darrell T, Keutzer K (2014) Densenet: Implementing efficient convnet descriptor pyramids. arXiv:1404.1869

  • Jain AK, Bolle R, Pankanti S (eds) (2006) Biometrics: personal identification in networked society, vol 479. Springer Science & Business Media, Berlin

    Google Scholar 

  • Johnson R, Zhang T (2013) Accelerating stochastic gradient descent using predictive variance reduction. Adv Neural Inf Process Syst 26:315–323

    Google Scholar 

  • Karouni A, Daya B, Bahlak S (2011) Offline signature recognition using neural networks approach. Proc Comput Sci 3:155–161

    Article  Google Scholar 

  • Khan FA, Tahir MA, Khelifi F, Bouridane A, Almotaeryi R (2017) Robust off-line text independent writer identification using bagged discrete cosine transform features. Expert Syst Appl 71:404–415

    Article  Google Scholar 

  • Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980

  • Kleber F, Fiel S, Diem M, 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, IEEE, pp 560–564

  • Kutzner T, Pazmiño-Zapatier CF, Gebhard M, Bönninger I, Plath WD, Travieso CM (2019) Writer identification using handwritten cursive texts and single character words. Electronics 8(4):391

    Article  Google Scholar 

  • Mahmoud SA, Luqman H, Al-Helali BM, BinMakhashen G, Parvez MT (2018) Online-khatt: an open-vocabulary database for Arabic online-text processing. Open Cybern Syst J 12:42–59

  • Marti UV, Bunke H (2002) The IAM-database: an English sentence database for offline handwriting recognition. Int J Doc Anal Recogn 5(1):39–46

    Article  MATH  Google Scholar 

  • Mousavi Z, Rezaii TY, Sheykhivand S, Farzamnia A, Razavi SN (2019) Deep convolutional neural network for classification of sleep stages from single-channel EEG signals. J Neurosci Methods 324:108312

    Article  Google Scholar 

  • Mousavi Z, Ettefagh MM, Sadeghi MH, Razavi SN (2020a) Developing deep neural network for damage detection of beam-like structures using dynamic response based on FE model and real healthy state. Appl Acoust 168:107402

    Article  Google Scholar 

  • Mousavi Z, Varahram S, Ettefagh MM, Sadeghi MH, Razavi SN (2020b) Deep neural networks–based damage detection using vibration signals of finite element model and real intact state: An evaluation via a lab-scale offshore jacket structure. Structural Health Monitoring, p. 1475921720932614.

  • Pechwitz M, Maddouri SS, Märgner V, Ellouze N, Amiri H (2002) IFN/ENIT-database of handwritten Arabic words. . Proc CIFED Citeseer 2:127–136

    Google Scholar 

  • Pokhriyal N, Tayal K, Nwogu I, Govindaraju V (2016) Cognitive-biometric recognition from language usage: a feasibility study. IEEE Trans Inf Forensics Secur 12(1):134–143

    Article  Google Scholar 

  • Rehman A, Naz S, Razzak MI, Hameed IA (2019) Automatic visual features for writer identification: a deep learning approach. IEEE Access 7:17149–17157

    Article  Google Scholar 

  • Rosasco L, Vito ED, Caponnetto A, Piana M, Verri A (2004) Are loss functions all the same? Neural Comput 16(5):1063–1076

    Article  MATH  Google Scholar 

  • Saba T (2018) Fuzzy ARTMAP approach for arabic writer identification using novel features fusion. J Comput Sci 14(2):210–220

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  • Santos P, Villa LF, Reñones A, Bustillo A, Maudes J (2015) An SVM-based solution for fault detection in wind turbines. Sensors 15(3):5627–5648

    Article  Google Scholar 

  • Schneiderpen (2017) https://schneiderpen.com/en_us/office/tops-505-black-f-4004675004529.pdf/

  • Shahabi F, Rahmati M (2009) A new method for writer identification of handwritten Farsi documents. In: 2009 10th International conference on document analysis and recognition. IEEE, pp 426–430

  • Sheykhivand S, Mousavi Z, Rezaii TY, Farzamnia A (2020a) Recognizing emotions evoked by music using CNN-LSTM networks on EEG signals. IEEE Access 8:139332–139345

    Article  Google Scholar 

  • Sheykhivand S, Rezaii TY, Mousavi Z, Delpak A, Farzamnia A (2020b) Automatic identification of epileptic seizures from EEG signals using sparse representation-based classification. IEEE Access 8:138834–138845

    Article  Google Scholar 

  • Sheykhivand S, Mousavi Z, Mojtahedi S, Rezaii TY, Farzamnia A, Meshgini S, Saad I (2021) Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images. Alex Eng J 60(3):2885–2903

    Article  Google Scholar 

  • Shreekanth T, Kumar MP, Krishnan A (2018) Text-independent handwriting classification using line and texture-based features. International conference on ISMAC in computational vision and bio-engineering. Springer, Cham, pp 211–220

    Google Scholar 

  • Srihari SN, Cha SH, Arora H, Lee S (2002) Individuality of handwriting. J Forensic Sci 47(4):1–17

    Article  Google Scholar 

  • Steyerberg EW (2019) Overfitting and optimism in prediction models. Clinical prediction models. Springer, Cham, pp 95–112

    Chapter  Google Scholar 

  • Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the AAAI conference on artificial intelligence 31(1)

  • Tang P, Wang H, Kwong S (2017) G-MS2F: GoogLeNet based multi-stage feature fusion of deep CNN for scene recognition. Neurocomputing 225:188–197

    Article  Google Scholar 

  • Wahlberg F (2018) Gaussian process classification as metric learning for forensic writer identification. In: 2018 13th IAPR International workshop on document analysis systems (DAS), IEEE, pp 175–180

  • Wójcik B, Maziarka Ł, Tabor J (2019) LOSSGRAD: automatic learning rate in gradient descent. arXiv:1902.07656

  • Wu X, Tang Y, Bu W (2014) Offline text-independent writer identification based on scale invariant feature transform. IEEE Trans Inf Forensics Secur 9(3):526–536

    Article  Google Scholar 

  • Wu Z, Shen C, Van Den Hengel A (2019) Wider or deeper: revisiting the resnet model for visual recognition. Pattern Recogn 90:119–133

    Article  Google Scholar 

  • 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), IEEE, pp 584–589

  • 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

    Article  Google Scholar 

  • Zhang W, Li C, Peng G, Chen Y, Zhang Z (2018) A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mech Syst Signal Process 100:439–453

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, 57169-63896, Urmia, Iran

    Seyed Nadi Mohamed Khosroshahi, Seyed Naser Razavi, Amin Babazadeh Sangar & Kambiz Majidzadeh

  2. Faculty of Electrical and Computer Engineering, University of Tabriz, 51666-16471, Tabriz, Iran

    Seyed Naser Razavi

Authors
  1. Seyed Nadi Mohamed Khosroshahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seyed Naser Razavi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amin Babazadeh Sangar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kambiz Majidzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seyed Naser Razavi.

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

Khosroshahi, S.N.M., Razavi, S.N., Sangar, A.B. et al. Deep neural networks-based offline writer identification using heterogeneous handwriting data: an evaluation via a novel standard dataset. J Ambient Intell Human Comput 13, 2685–2704 (2022). https://doi.org/10.1007/s12652-021-03253-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-021-03253-2

Keywords

Search

Navigation