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Directional Hinge Features for Writer Identification: The Importance of the Skeleton and the Effects of Character Size and Pixel Intensity

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Abstract

Directional features such as Skeleton Hinge Distribution are not computationally expensive, are fast, easy to explain and very efficient in identifying the writer of a handwritten text. Hinge Distribution techniques are responsible for several works that have been researched in the literature recently. In this work, an attempt is made to evaluate the importance of three factors, the skeleton information, information regarding the size of the text and information regarding the grey-scale intensity of the text, that might affect writer identification accuracy in applications that utilize Directional features. Towards that goal, four new Hinge Distribution features are suggested. More specifically, the Run Length Directional Hinge Distribution (RLDHD) considers all the available pixel information in the text and the Run Length Skeleton Directional Hinge Distribution (RLSDHD), a variation of the former that utilizes the skeleton information. Furthermore, the Weighted Skeleton Hinge Distribution (WSHD) method considers the text size using the Main Body Size estimating technique and the Quantized Skeleton Hinge Distribution (QSHD) that utilizes the grey-scale intensity of the text. For the evaluation, the Firemaker Data set, IAM data set, CVL data set and the ICDAR 2017 writer identification competition Data set were considered. Our results indicate that Skeleton information plays a vital role in writer identification. On the other hand, Main Body Size fluctuations do not seem to affect identification accuracy. Finally, considering the grey-scale intensity, results seem inconclusive, and further research might be necessary.

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References

  1. Schomaker L. Advances in writer identification and verification. Ninth International Conference on Document Analysis and Recognition. 2007. pp. 1268–1273.

  2. Vielhauer C. Biometric user authentication for IT security: from fundamentals to handwriting. Berlin: Springer Science & Business Media; 2005. (Vol. 18).

    Google Scholar 

  3. Diamantatos P, Kavallieratou E, Gritzalis S. Skeleton Hinge Distribution for writer identification. Int J Artif Intell Tools. 2016;25:1650015.

    Article  Google Scholar 

  4. Bulacu M, Schomaker L, Vuurpijl L. Writer identification using edge-based directional features. IEEE, 2003.

  5. Schomaker L, Vuurpijl L. Forensic writer identification: a benchmark data set and a comparison of two systems. Internal report for the Netherlands Forensic Institute. Technical report, Nijmegen: NICI, 2000.

  6. He S, Schomaker L. Co-occurrence features for writer identification. In: 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 78–83.

  7. Fiel S, Kleber F, Diem M, Christlein V, Louloudis G, Nikos S, Gatos B. Icdar2017 competition on historical document writer identification (historical-wi). In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), pp. 1377–1382.

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

    Article  Google Scholar 

  9. Van Der Maaten L, Postma EO. Improving automatic writer identification. BNAIC, 2005.

  10. He S, Schomaker L. Delta-n hinge: rotation-invariant features for writer identification. In: 2014 22nd International Conference on Pattern Recognition, 2014, August.

  11. Brink AA, Smit J, Bulacu ML, Schomaker LRB. Writer identification using directional ink-trace width measurements. Pattern Recogn. 2012;45(1):162–71.

    Article  Google Scholar 

  12. Said HE, Tan TN, Baker KD. Personal identification based on handwriting. Pattern Recogn. 2000;33(1):149–60.

    Article  Google Scholar 

  13. Zois EN, Anastassopoulos V. Morphological waveform coding for writer identification. Pattern Recogn. 2000;33(3):385–98.

    Article  Google Scholar 

  14. Srihari SN, Beal MJ, Bandi K, Shah V, Krishnamurthy P. A statistical model for writer verification. Proc. Eighth Int’l Conf. Document Analysis and Recognition (ICDAR). 2005. pp. 1105–1109.

  15. Bensefia A, Paquet T, Heutte L. Handwritten document analysis for automatic writer recognition. Electronic Lett Comput Vision Image Anal. 2005;5(2):72–86.

    Article  Google Scholar 

  16. Schomaker L, Bulacu M, Franke K. Automatic writer identification using fragmented connected-component contours. In Proceedings of the 9th IWFHR. Tokyo, Japan, 2004. pp. 185–190.

  17. Schlapbach A, Bunke H. A writer identification and verification system using HMM based recognizers. Pattern Anal Appl (Springer). 2007;10:33–43. https://doi.org/10.1007/s10044-006-0047-5.

    Article  MathSciNet  Google Scholar 

  18. Pervouchine V, Leedham G. Extraction and analysis of forensic document examiner features used for writer identification. Pattern Recogn J. 2007;40:1004–13.

    Article  Google Scholar 

  19. Bar-Yosef I, Beckman I, Kedem K, Dinstein I. Binarization, character extraction, and writer identification of historical Hebrew calligraphy documents. Int J Doc Anal Recogn. 2007;9(2):89–99.

    Article  Google Scholar 

  20. He Z, You X, Tang YY. Writer identification of Chinese handwriting documents using hidden Markov tree model. Pattern Recogn J. 2008;41:1295.

    Article  Google Scholar 

  21. Yan Y, Chen Q, Deng W, Yuan F. Chinese handwriting identification based on stable spectral feature of texture images. Int J Intell Eng Syst. 2009;2(1):17.

    Google Scholar 

  22. Bulacu M, Schomaker L. Text-independent writer identification and verification using textural and allographic features. IEEE Trans Pattern Anal Mach Intell (PAMI). 2007;29(4):701–17.

    Article  Google Scholar 

  23. Al-Dmour A, Zitar R. Arabic writer identification based on hybrid spectral- statistical measures. J Exp Theor Artif Intell. 2007;19(4):307–32.

    Article  Google Scholar 

  24. Wu X, Tang Y, Bu W. Offline text-independent writer identification based on scale invariant feature transform. IEEE Trans Inf Forensics Secur. 2014;9(3):526–36.

    Article  Google Scholar 

  25. Nicolaou A, Bagdanov AD, Liwicki M, KaratzasD. Sparse radial sampling LBP for writer identification. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 716–720.

  26. Mohammed H, Mäergner V, Konidaris T, Stiehl HS. Normalised Local Naïve Bayes Nearest-Neighbour Classifier for Offline Writer Identification. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), pp. 1013–1018.

  27. Newell AJ, Griffin LD. Writer identification using oriented basic image features and the delta encoding. Pattern Recogn. 2014;47(6):2255.

    Article  Google Scholar 

  28. Abdeljalil G, Djeddi C, Siddiqi I, Al-Maadeed S. Writer identification on historical documents using oriented basic image features. In: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR). pp. 369–373.

  29. Nadia F, Kamel H. Personal identification based on texture analysis of arabic handwriting text. In: IEEE International Conference on Information and Communications Technologies (ICTTA’06). 2007. vol. (1), pp. 1302–1307.

  30. Gazzah S, Amara NB. Arabic Handwriting texture analysis for writer identification using the DWT-lifting scheme. In: 9th International Conference on Document Analysis and Recognition (ICDAR’07). 2007. vol. (2), pp. 1133–1137.

  31. Al-Ma'adeed S, Mohammed E, Al Kassis D, Al-Muslih F. Writer identification using edge-based directional probability distribution features for Arabic words. In: IEEE/ACS International Conference on Computer Systems and Applications (AICCSA). 2008. p. 582–590.

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  34. Fiel S, Sablatnig R. Writer identification and retrieval using a convolutional neural network. International Conference on Computer Analysis of Images and Patterns Springer, Cham, 2015.

  35. Xing L, Qiao Y. Deepwriter: A multi-stream deep CNN for text-independent writer identification. 15th international conference on frontiers in handwriting recognition (ICFHR). IEEE, 2016.

  36. Tang Y, Wu X. Text-independent writer identification via CNN features and joint Bayesian. 15th International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, 2016.

  37. Khan FA, Khelifi F, Tahir MA, Bouridane A. Dissimilarity Gaussian mixture models for efficient offline handwritten text-independent identification using SIFT and RootSIFT descriptors. IEEE Trans Inf Forensics Secur. 2019;14(2):289–303.

    Article  Google Scholar 

  38. He S, Schomaker L. Deep adaptive learning for writer identification based on single handwritten word images. Pattern Recogn. 2019;88:64–74.

    Article  Google Scholar 

  39. He S, Schomaker L. Fragnet: writer identification using deep fragment networks. IEEE Trans Inf Forensics Secur. 2020;15:3013–22.

    Article  Google Scholar 

  40. He S, Schomaker L. GR-RNN: global-context residual recurrent neural networks for writer identification. Pattern Recognit. 2021;117:107975.

    Article  Google Scholar 

  41. Popović M, Dhali MA, Schomaker L. Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa). PLoS ONE. 2021;16(4):e0249769.

    Article  Google Scholar 

  42. Rehman A, Naz S, Razzak MI. Writer identification using machine learning approaches: a comprehensive review. Multimed Tools Appl. 2019;78(8):10889–931.

    Article  Google Scholar 

  43. E Commission. Europe fit for the Digital Age: Artificial Intelligence. [Online]. Available: https://ec.europa.eu/commission/presscorner/detail/en/ip_21_1682. Accessed 1 May 2021.

  44. Schomaker L. Dilemmas in the application of artificial- intelligence methods in digital paleography. [Online]. Available: https://www.youtube.com/watch?v=chVdYOBnOuw. Accessed 1 May 2021.

  45. Diamantatos P, Verras V, Kavallieratou E. Detecting Main Body Size in Document Images. Document Analysis and Recognition (ICDAR), 12th International Conference on IEEE, 2013.

  46. Alaei A, Pal U, Nagabhushan P. A new scheme for unconstrained handwritten text-line segmentation. Pattern Recogn. 2011;44(4):917–28.

    Article  Google Scholar 

  47. Gonzalez RC, Woods RE, Eddins SL. Digital image processing using Matlab. New Jersey: Princeton Hall Pearson Education Inc.; 2004.

    Google Scholar 

  48. Marti U, Bunke H. The IAM-database: an english sentence database for off-line handwriting recognition. Int J Doc Anal Recogn. 2002;5:39–46.

    Article  Google Scholar 

  49. Kleber F, Fiel S, Diem M, Sablatnig R. CVL-Database: An off-line database for writer retrieval, writer identification and word spotting. In: Proc. of the 12th Int. Conference on Document Analysis and Recognition (ICDAR), 2013. p. 56.

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Correspondence to Paraskevas Diamantatos.

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Diamantatos, P., Kavallieratou, E. & Gritzalis, S. Directional Hinge Features for Writer Identification: The Importance of the Skeleton and the Effects of Character Size and Pixel Intensity. SN COMPUT. SCI. 3, 56 (2022). https://doi.org/10.1007/s42979-021-00950-9

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