Writer Identification on Multi-Script Handwritten Using Optimum Features

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Ahmed Abdullah Ahmed Harith Raad Hasan Fariaa Abdalmajeed Hameed Omar Ismael Al-Sanjary


Recognizing the writer of a text that has been handwritten is a very intriguing research problem in the field of document analysis and recognition. This study tables an automatic way of recognizing the writer from handwritten samples. Even though much has been done in previous researches that have presented other various methods, it is still clear that the field has a room for improvement. This particular method uses Optimum Features based writer characterization. Here, each of the samples written is grouped according to their set of features that are acquired from a computed codebook. This proposed codebook is different from the others which segment the samples into graphemes by fragmenting a certain part of the writing known as ending strokes. The proposed technique is employed to a locate and extract the handwriting fragments from ending zone and then grouped the similar fragments to generate a new cluster known as ending cluster. The cluster that comes in handy in the process of coming up with the ending codebook through picking out the center of the same fragment group. The process is finalized by evaluating the proposed method on four datasets of the various languages. This method being proposed had an impressive 97.12% identification rate which is rates the best result on the ICFHR dataset.


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[1]. D. Chawki, L. Souici-Meslati, "A texture based approach for Arabic Writer Identification and Verification," In Machine and Web Intelligence (ICMWI), International Conference on IEEE, pp. 115-120, 2010.
[2]. U. Garain, T. Paquet, “Off-line Multi-Script Writer Identification using AR Coefficients, ’’ In Proc of the International Conference on Document Analysis and Recognition, Spain, pp. 991-995, 2009.
[3]. D. Chawki, I. Siddiqi, L. Souici-Meslati, A. Ennaji, ‘‘Multi Script Writer Identification Optimized with Retrieval Mechanism,’’ In Proc. of the International Conference on Frontiers Handwriting Recognition, Bari, Italy, pp. 507 – 512, 2012.
[4]. D. Chawki, L. Souici-Meslati, A. Ennaji, ‘‘Writer Recognition on Arabic Handwritten Documents’’, In Proc. of the International Conference on Image and Signal Processing, Agadir, Morocco, pp 493-501, June 2012.
[5]. L. Schomaker, M. Bulacu, “Automatic Writer Identification Using Connected-Component Contours and Edge-Based Features of Upper-Case Western Script,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(6), pp. 787–798, 2004.
[6]. I. Siddiqi, N. Vincent, “Writer Identification in Handwritten Documents,” In Proc. of the 9th International Conference on Document Analysis and Recognition, 1, pp. 108–112, 2007.
[7]. R. Jain, D. Doermann, "Offline Writer Identification using K-Adjacent Segments," In Proc. of the 11th International Conference on Document Analysis and Recognition (ICDAR'11) on IEEE, pp. 769-773, 2011.
[8]. A.A. Ahmed, G. Sulong, "Arabic Writer Identification: A Review of Literature," Journal of Theoretical & Applied Information Technology 69(3), 2014.
[9]. I. Siddiqi, N. Vincent, “Text independent writer recognition using redundant writing patterns with contour-based orientation and curvature features,” In Pattern Recognition, 43(11), pp. 3853 – 3865, 2010.
[10]. M. Bulacu, L. Schomaker, "Text-independent writer identification and verification using textural and allographic features," IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Special Issue - Biometrics: Progress and Directions, IEEE Computer Society, 29(4), pp. 701-717, 2007.
[11]. A. Bensefia, A. Nosary, T. Paquet, L. Heutte, “Writer identification by writer's invariants,” In Proc. of the International Workshop on Frontiers in Handwriting Recognition, pp. 274-279, 2002.
[12]. A. Bensefia, T. Paquet, L. Heutte, “A writer identification and verification system,” In Pattern Recognition Letters, 26(13), pp. 2080 – 2092, 2005.
[13]. G. Louloudis, N. Stamatopoulos, B. Gatos, “ICDAR 2011 - Writer Identification Contest,” In Proc of the 11th International Conference on Document Analysis and Recognition, pp. 1475-1479, China, 2011.
[14]. U. Marti, H. Bunke, “The IAM-database: an English sentence database for offline handwriting recognition,” In International Journal on Document Analysis and recognition, 5(1), pp. 1433–2825, 2002.
[15]. A. Hassaïne, S. Al-Maadeed, “ICFHR2012 competition on writer identification - Challenge 2: Arabic scripts,” In Frontiers in Handwriting Recognition (ICFHR), International Conference on IEEE, pp. 835-840, 2012.
[16]. N. Otsu, "A threshold selection method from gray-level histograms," IEEE transactions on systems, man, and cybernetics 9(1), pp. 62-66, 1979.
[17]. P. Sharma, M. Diwakar, N. Lal, “Edge Detection using Moore Neighborhood,” International Journal of Computer Applications, 61(3), pp. 26–30, 2013.

[18]. I. Siddiqi, N. Vincent, “Combining Global and Local Features for Writer Identification,” in Proceedings of the 11. Int. Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 19–21, 2008.
[19]. E.C. Djamal, R. Darmawati, S.N. Ramdlan, “Application Image Processing to Predict Personality Based on Structure of Handwriting and Signature,” in International Conference on Computer, Control, Informatics and Its Applications, pp. 163–168, 2013.
[20]. N. Mogharreban, S. Rahimi M. Sabharwal. "A combined crisp and fuzzy approach for handwriting analysis," Fuzzy Information, 2004. Processing NAFIPS'04. IEEE Annual Meeting, 1, pp. 351-356, 2004.
[21]. L. Schomaker, M. Bulacu, K. Franke, “Automatic writer identification using fragmented connected-component contours,” Proc. - Int. Work. Front. Handwrit. Recognition, IWFHR, pp. 185–190, 2004.
[22]. M. Bulacu, L. Schomaker, A. Brink, “Text-independent writer identification and verification on offline arabic handwriting,” Proc. Int. Conf. Doc. Anal. Recognition, ICDAR, 29(4), pp. 769–773, 2007.
[23]. A.J. Newell, L.D. Griffin, “Writer Identification Using Oriented Basic Image Features and the Delta Encoding,” Pattern Recognition, 47(6), pp. 2255–2265, Jun. 2014.
[24]. L. V. D. Maaten, E. Postma, “Improving automatic writer identification,” in Proc. of 17th Belgium-Netherlands Conference on Artificial Intelligence (BNAIC), pp. 260–266, 2005.
[25]. A.J. Newell, L.D. Griffin, “Natural image character recognition using oriented basic image features,” in Proceedings - 2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA, pp. 191–196, 2011.
[26]. H.J. Escalante, T. Solorio, M.M. Gómez, “Local Histograms of Character n-grams for Authorship Attribution,” Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 1, pp. 288–298, 2011.