The method of probabilistic nodes combination in handwriting recognition

• Autorzy: Jakóbczak D. J.
• Języki publikacji: EN

Abstrakty

EN

Proposed method, called Probabilistic Nodes Combination (PNC), is the method of 2D curve modeling and handwriting identification by using the set of key points. Nodes arę treated as characteristic points of signature or handwriting for modeling and writer recognition. Identification of handwritten letters or symbols need modeling and the model of each individual symbol or character is built by a choice of probability distribution function and nodes Combination. PNC modeling via nodes combination and parameter y as probability distribution function enables curve parameterization and interpolation for each specific letter or symbol. Two-dimensional curve is modeled and interpolated via nodes combination and different functions as continuous probability distribution functions: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function.

PL

Zaproponowana autorska metoda Probabilistycznej Kombinacji WęzłówProbabilistic Nodes Combination (PNC) jest sposobem modelowania krzywej 2D oraz identyfikacji i rozpoznania pisma odręcznego na podstawie punktów kluczowych (węzłów). Węzły traktowane są jako punkty charakterystyczne podpisu odręcznego lub pisma przed modelowaniem i rozpoznaniem. Dwuwymiarowe dane są interpolowane z wykorzystaniem różnych funkcji rozkładu prawdopodobieństwa: potęgowych, wielomianowych, wykładniczych, logarytmicznych, trygonometrycznych, cyklometrycznych. W pracy pokazano propozycję algorytmu modelowania i rozpoznania pisma odręcznego.

Słowa kluczowe

EN

Hurwitz-Radon matrices; Active Object Modeling

PL

macierze Hurwitza-Radona; modelowanie obiektów

• Wydawca: Wydawnictwo Uczelniane Politechniki Koszalińskiej
• Czasopismo: Zeszyty Naukowe Wydziału Elektroniki i Informatyki Politechniki Koszalińskiej
• Rocznik: 2014
• Tom: Nr 6
• Strony: 35--50
• Opis fizyczny: Bibliogr. 29 poz., wykr.

Twórcy

autor

Jakóbczak D. J.

• Katedra Podstaw Informatyki i Zarządzania, Wydział Elektroniki i Informatyki, Politechnika Koszalińska

Bibliografia

• 1. Schlapbach, A., Bunke, H.: Off-line writer identification using Gaussian mixture models. In: International Conference on Pattem Recognition, pp. 992-995 (2006)
• 2. 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)
• 3. Djeddi, C., Souici-Meslati, L.: A texture based approach for Arabie writer identification and verification. In: International Conference on Machinę and Web Intelligence, pp. 115-120 (2010)
• 4. Djeddi, C., Souici-Meslati, L.: Artificial immune recognition system for Arabie writer identification. In: International Symposium on Innovation in Information and Communication Technology, pp. 159-165 (2011)
• 5. Nosary, A., Heutte, L., Paąuet, T.: Unsupervised writer adaption applied to handwritten text recognition. Pattern Recogn. Lett. 37 (2), 385-388 (2004)
• 6. Van, E.M., Yuurpijl, L., Frankę, K., Schomaker, L.: The WANDA measurement tool for forensic document examination. J. Forensic Doc. Exam. 16,103-118(2005)
• 7. Schomaker, L., Frankę, K., Bulacu, M.: Using codebooks of fragmented connected-component contours in forensic and historie writer identification. Pattern Recogn. Lett. 28 (6), 719-727 (2007)
• 8. Siddiąi, L, Cloppet, F., Yincent, N.: Contour based features for the classification of ancient manuscripts. In: Conference of the International Graphonomics Society, pp. 226-229 (2009)
• 9. Garain, U., Paquet, T.: Off-line multi-script writer identification using AR coefficients. In: International Conference on Document Analysis and Recognition, pp. 991-995 (2009)
• 10. Bulacu, M., Schomaker, L., Brink, A.: Text-independent writer identification and verification on off-line Arabie handwriting. In: International Conference on Document Analysis and Recognition, pp. 769-773 (2007)
• 11. Ozaki, M., Adachi, Y., Ishii, N.: Examination of effects of character size on accuracy of writer recognition by new local arc method. In: International Conference on Knowledge-Based Intelligent Information and Engineering Systems, pp.l 170-1175 (2006)
• 12. Chen, J., Lopresti, D., Kavallieratou, E.: The impact of ruling lines on writer identification. In: International Conference on Frontiers in Handwriting Recognition, pp. 439-444 (2010)
• 13. Chen, J., Cheng, W., Lopresti, D.: Using perturbed handwriting to support writer identification in the presence of severe data constraints. In: Document Recognition and Retrieval, pp. 1-10 (2011)
• 14. Galloway, M.M.: Texture analysis using gray level run lengths. Comput. Graphics Image Process. 4 (2), 172-179 (1975)
• 15. Siddiąi, I., Yincent, N.: Text independent writer recognition using redundant writing patterns with contour-based orientation and curvature features. Pattern Recogn. Lett. 43 (11), 3853-3865 (2010)
• 16. Ghiasi, G., Safabakhsh, R.: Offline text-independent writer identification using codebook and efficient code extraction methods. Image and Yision Computing 31,379-391 (2013)
• 17. Shahabinejad, R, Rahmati, M.: A new method for writer identification and verification based on Farsi/Arabic handwritten texts, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), pp. 829-833(2007)
• 18. Schlapbach, A., Bunke, H.: A writer identification and verification system using HMM based recognizers, Pattern Anal. Appl. 10, 33^13 (2007)
• 19. Schlapbach, A., Bunke, H.: Using HMM based recognizers for writer identification and verification, 9th Int. Workshop on Frontiers in Handwriting Recognition, pp. 167-172 (2004)
• 20. Marti, U.-V., Bunke, H.: The lAM-database: an English sentence database for offline handwriting recognition, Int. J. Doc. Anal. Recognit. 5, 39-46 (2002)
• 21. Collins II, G.W.: Fundamental Numerical Methods and Data Analysis. Case Western Reserve University (2003)
• 22. Chapra, S.C.: Applied Numerical Methods. McGraw-Hill (2012)
• 23. Ralston, A., Rabinowitz, P.: A First Course in Numerical Analysis - Second Edition. Dover Publications, New York (2001)
• 24. Zhang, D., Lu, G.: Review of Shape Representation and Description Techniąues. Pattern Recognition 1(37), 1-19 (2004)
• 25. Schumaker, L.L.: Spline Functions: Basic Theory. Cambridge Mathematical Library (2007)
• 26. Dahlquist, G., Bjoerck, A.: Numerical Methods. Prentice Hali, New York (1974)
• 27. Jakóbczak, D.: 2D and 3D Image Modeling Using Hurwitz-Radon Matrices. Polish Journal of Environmental Studies 4A(16), 104-107 (2007)
• 28. Jakóbczak, D.: Shape Representation and Shape Coefficients via Method of Hurwitz-Radon Matrices. Lecture Notes in Computer Science 6374 (Computer Vision and Graphics: Proc. ICCYG 2010, Part I), Springer-Verlag Berlin Heidelberg, 411-419 (2010)
• 29.Jakóbczak, D.: Curve Interpolation Using Hurwitz-Radon Matrices. Polish Journal of Environmental Studies 3B(18), 126-130 (2009)