HMM-Based Off-Line Uyghur Signature Recognition

  • Long-Fei Mo
  • Hornisa Mamat
  • Mutallip Mamut
  • Alimjan Aysa
  • Kurban UbulEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)


Signature as a new biometric-based feature, due to its convenience, reliability, and non-invasion, signature recognition has been accepted by people. It is widely used in many fields such as commercial, financial, judicial, insurance and other aspects, so offline signature recognition has important theoretical significance and practical value. In this paper, an offline signature recognition system based on Hidden Markov Models is established to extract the DCT features of off-line signatures. This method takes all the fonts in the offline signature image as a whole, uses image processing techniques to segment the entire font area, and then calculates the number of pixels in each font part. The whole is modeled by a Hidden Markov Model, the best state chain is obtained using viterbi segmentation, and the EM algorithm is used to train the model. There are 2000 Uyghur signatures from 100 different people, 1000 English signatures from 50 different people, the highest recognition rates were 99.5% and 97.5%, respectively. The experimental results show that Hidden Markov Model can accurately describe the characteristics of Uygur signatures.


Offline signature Hidden Markov Model Discrete cosine transform 



This work was supported by the National Natural Science Foundation of China (No. 61563052, 61163028, 61363064), the Funds for Creative Research Groups of Higher Education of Xinjiang Uyghur Autonomous Region (XJEDU2017T002).


  1. 1.
    Basil, M., Gawali, B.: Comparative analysis of MSER and DTW for offline signature recognition. Int. J. Comput. Appl. (0975–8887) 110(5) (2015)Google Scholar
  2. 2.
    Marušić, T., Marušić, Ž., Šeremet, Ž.: Identification of authors of documents based on offline signature recognition. In: MIPRO 2015, 25–29 May 2015, Opatija, Croatia (2015)Google Scholar
  3. 3.
    Zeinstra, C.G., Meuwly, D., Ruifrok, A.C., Veldhuis, R.N., Spreeuwers, L.J.: Forensic face recognition as a means to determine strength of evidence: a survey. Forensic Sci. Rev. 30(1), 21–32 (2018)Google Scholar
  4. 4.
    Ribeiro, B., Gonçalves, I., Santos, S., Kovacec, A.: Deep learning networks for off-line handwritten signature recognition. In: San Martin, C., Kim, S.-W. (eds.) CIARP 2011. LNCS, vol. 7042, pp. 523–532. Springer, Heidelberg (2011). Scholar
  5. 5.
    Zhang, Y., Xu, Y., Bao, H.: Offline handwritten signature recognition method based on multi-features. J. Converg. Inf. Technol. 8(5) (2013)Google Scholar
  6. 6.
    Kudłacik, P., Porwik, P.: A new approach to signature recognition using the fuzzy method. Pattern Anal. Appl. 17, 451–463 (2014)Google Scholar
  7. 7.
    Hafemann, L.G., Luiz, R.S., Oliveira, S.: Offline Handwritten Signature Verification-Literature Review. arXiv:1507.07909v2 [cs.CV], August 2015
  8. 8.
    Bertolini, D., Oliveira, L.S., Justino, E., Sabourin, R.: Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers. Pattern Recogn. 43(1), 387–396 (2010)Google Scholar
  9. 9.
    Abril, G.: Uyghur Offline Signature Recognition Technology. Xinjiang University (2012)Google Scholar
  10. 10.
    Serdouk, Y., Nemmour, H., Chibani, Y.: New off-line handwritten signature verification method based on artificial immune recognition system. Expert Syst. Appl. 51, 186–194 (2016)Google Scholar
  11. 11.
    Yi, A.Y.: Uyghur Offline Handwritten Signature Recognition. Xinjiang University (2014)Google Scholar
  12. 12.
    Justino, E.J.R., Bortolozzi, F., Sabourin, R.: A comparison of SVM and HMM classifiers in the off-line signature verification. Pattern Recogn. Lett. 26(9), 1377–1385 (2005)Google Scholar
  13. 13.
    Kessentini, Y., Burger, T., Paquet, T.: A Dempster-Shafer theory based combination of handwriting recognition systems with multiple rejection strategies. Pattern Recogn. 48(2), 534–544 (2015)Google Scholar
  14. 14.
    Kumar, R., Sharma, J.D., Chanda, B.: Writer-independent off-line signature verification using surroundedness feature. Pattern Recogn. Lett. 33(3), 301–308 (2012)Google Scholar
  15. 15.
    Aini, Z.: Research on Uyghur Off-Line Handwritten Signature Authentication Based on Statistical Features. Xinjiang University (2017)Google Scholar
  16. 16.
    Yimin, A.: Research on Uyghur Handwritten Signature Recognition Based on Multiple Features. Xinjiang University (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Long-Fei Mo
    • 1
  • Hornisa Mamat
    • 1
  • Mutallip Mamut
    • 2
  • Alimjan Aysa
    • 3
  • Kurban Ubul
    • 1
    Email author
  1. 1.School of Information Science and EngineeringXinjiang UniversityUrumqiChina
  2. 2.The Library of Xinjiang UniversityUrumqiChina
  3. 3.The Network and Information Center of Xinjiang UniversityUrumqiChina

Personalised recommendations