Abstract
The problem of authenticating a writer from his/her writing samples has been the most important and prevalent one subject of active research in the field of handwriting biometrics for the last decade. In this paper, we have focused mainly on the forensic document analysis, more precisely, the offline automatic writer identification in a truly text-independent mode. A new and simple potential textural descriptor has been analyzed for characterizing the handwriting style of the writers, so as to be used to describe the intra and inter-writer variability by calculating the similarity measurements. In order to extract the textural properties from a scanned handwritten sample, an effective statistical texture descriptor is computed from binary connected-components: Local Black Pattern (LBLP). Classification is performed using k-Nearest Neighbors (k-NN) and the Chi-Square (\(\chi ^2\)) distance in a Holdout strategy. The experimental results obtained on two well-known databases show that the proposed scheme achieves a very satisfactory performance and thus reflecting that our approach is still competitive against the state-of-the-art.
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References
Franke, K., Köppen, M.: A computer-based system to support forensic studies on handwritten documents. Int. J. Doc. Anal. Recogn. 3, 218–231 (2001)
Srihari, S.N., Cha, S.H., Lee, S., Arora, H.: Individuality of handwriting. J. Forensic Sci. 47(4), 856–872 (2002)
Pechwitz, M., Maddouri, S.S., Märgner, V., Ellouze, N., Amiri, H.: IFN/ENIT database of handwritten Arabic words. In: \(7^{th}\) Colloque International Francophone sur l’Ecrit et le Documentn, CIFED 2002, pp. 129–136. Hammamet, Tunis (2002)
Marti, U.V., Bunke, H.: The IAM-database: an English sentence database for offine handwriting recognition. Int. J. Doc. Anal. Recogn. IJDAR 5, 39–46 (2002). https://doi.org/10.1007/s100320200071
Bulacu, M., Schomaker, L.: Text-independent writer identification and verification using textural and allographic features. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 701–717 (2007)
He, S., Schomaker, L.: Writer identification using curvature-free features. Pattern Recogn. 63, 451–464 (2017)
Bertolini, D., Oliveira, L.S., Justino, E., Sabourin, R.: Texture-based descriptors for writer identification and verification. Expert Syst. Appl. 40(6), 2069–2080 (2013)
Wu, X., Tang, Y., Bu, W.: Offline text-independent writer identification based on scale invariant feature transform. IEEE Trans. Inf. Forensics Secur. 9(3), 526–536 (2014)
Bahram, T., Benyettou, A., Ziadi, D.: A set of features for text-independent writer identification. Int. Rev. Comput. Softw. (I. RE. CO. S) 11(10), 898–906 (2016)
Bahram, T.: A connected component-based approach for text-independent writer identification. In: 2019 \(6^{th}\) International Conference on Image and Signal Processing and their Applications (ISPA2019), pp. 1–6. IEEE, Mostaganem, Algeria (2019)
Khan, F.A., Khelifi, F., Tahir, M.A.: Dissimilarity Gaussian mixture models for efficient offline handwritten text-independent identification using SIFT and RootSIFT descriptors. IEEE Trans. Inf. Forensics Secur. 14(2), 289–303 (2019)
Chahi, A., El-merabet, Y., Ruichek, Y., Touahni, R.: Local gradient full-scale transform patterns based off-line text-independent writer identification. Appl. Soft Comput. J. 92, 106277 (2020)
Hannad, Y., Siddiqi, I., El-Kettani, M.E.: Writer identification using texture descriptors of handwritten fragments. Expert Syst. Appl. 47, 14–22 (2016)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)
Ojansivu, V., Heikkilä, J.: Blur insensitive texture classification using local phase quantization. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008. LNCS, vol. 5099, pp. 236–243. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69905-7_27
Bahram, T.: A texture-based approach for offline writer identification. J. King Saud Univ. Comput. Inf. Sci. (2022). https://doi.org/10.1016/j.jksuci.2022.06.003
He, S., Schomaker, L.: FragNet: writer identification using deep fragment networks. IEEE Trans. Inf. Forensics Secur. 15, 3013–3022 (2020)
Kumar, P., Sharma, A.: Segmentation-free writer identification based on convolutional neural network. Comput. Electr. Eng. 85, 106707 (2020)
Lai, S., Zhu, Y., Jin, L.: Encoding Pathlet and SIFT FeaturesWith bagged VLAD for historical writer identification. IEEE Trans. Inf. Forensics Secur. 15, 3553–3566 (2020)
Semma, A., Hannad, Y., Siddiqi, I., Djeddi, C., El-Kettani, M.E.: Writer identification using deep learning with FAST Keypoints and Harris corner detector. Expert Syst. Appl. 184, 115473 (2021)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Acknowledgements
The authors are grateful to the anonymous referees for their valuable and helpful comments. This research has been carried out within the PRFU project (Grant: C00L07UN220120220001) of the Department of computer science, University Djillali Liabes of Sidi Bel-Abbes. The authors thank the staff of EEDIS and LGACA laboratories for helpful comments and suggestions.
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Bahram, T., Adjoudj, R. (2023). Offline Text-Independent Writer Identification Using Local Black Pattern Histograms. In: Salem, M., Merelo, J.J., Siarry, P., Bachir Bouiadjra, R., Debakla, M., Debbat, F. (eds) Artificial Intelligence: Theories and Applications. ICAITA 2022. Communications in Computer and Information Science, vol 1769. Springer, Cham. https://doi.org/10.1007/978-3-031-28540-0_19
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