Handwrittenword recognition using fuzzy matching degrees

• Autorzy: Wróbel Michał , Starczewski Janusz T. , Fijałkowska Justyna , Siwocha Agnieszka , Napoli Christian

Identyfikatory

DOI

10.2478/jaiscr-2021-0014

• Języki publikacji: EN

Abstrakty

EN

Handwritten text recognition systems interpret the scanned script images as text composed of letters. In this paper, efficient offline methods using fuzzy degrees, as well as interval fuzzy degrees of type-2, are proposed to recognize letters beforehand decomposed into strokes. For such strokes, the first stage methods are used to create a set of hypotheses as to whether a group of strokes matches letter or digit patterns. Subsequently, the second-stage methods are employed to select the most promising set of hypotheses with the use of fuzzy degrees. In a primary version of the second-stage system, standard fuzzy memberships are used to measure compatibility between strokes and character patterns. As an extension of the system thus created, interval type-2 fuzzy degrees are employed to perform a selection of hypotheses that fit multiple handwriting typefaces.

Słowa kluczowe

EN

offline handwriting recognition; handwritten strokes; fuzzy matching degrees; interval type-2 fuzzy sets; decision tree; bigram frequency

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2021
• Tom: Vol. 11, No. 3
• Strony: 229--242
• Opis fizyczny: Bibliogr. 3 poz., rys.

Twórcy

autor

Wróbel Michał

• Department of Computational Intelligence, Czestochowa University of Technology al. Armii Krajowej 36, 42-200 Częstochowa, Poland

autor

Starczewski Janusz T.

• Department of Computational Intelligence, Czestochowa University of Technology al. Armii Krajowej 36, 42-200 Częstochowa, Poland

autor

Fijałkowska Justyna

• Management Department University of Social Sciences, 90-113 Łod´z

autor

Siwocha Agnieszka

• Information Technology Institute University of Social Sciences, 90-113 Łod´z
• Clark University Worcester, MA 01610, USA

autor

Napoli Christian

• Department of Computer, Control and Management Engineering Sapienza University of Rome, Via Ariosto 25 Roma 00185, Italy

Bibliografia

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• [2] M. Jones and D. Mewhort. Case-sensitive letter and bigram frequency counts from large-scale english corpora. Behavior Research Methods, Instruments, & Computers, 36:388–396, 2004.
• [3] E. L’Homer. Extraction of strokes in handwritten characters. Pattern Recognition, 33:1147–1160, 2000.
• [4] J. M. Mendel. Computing with words and its relationships with fuzzistics. Information Sciences, 177(4):988–1006, 2007.
• [5] R. K. Nowicki and J. T. Starczewski. A new method for classification of imprecise data using fuzzy rough fuzzification. Inf. Sci., 414:33–52, 2017.
• [6] D. J. Ostrowski and P. Y. K. Cheung. A Fuzzy Logic Approach to Handwriting Recognition, pages 299–314. Vieweg+Teubner Verlag, Wiesbaden, 1996.
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• [8] J. T. Starczewski. Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty, volume 284 of Studies in Fuzziness and Soft Computing. Springer, 2013.
• [9] M. Wróbel, K. Nieszporek, J. T. Starczewski, and A. Cader. A fuzzy measure for recognition of handwritten letter strokes. In L. Rutkowski, R. Scherer, M. Korytkowski, W. Pedrycz, R. Tadeusiewicz, and J. M. Zurada, editors, Artificial Intelligence and Soft Computing, pages 761–770, Cham, 2018. Springer International Publishing.
• [10] M. Wróbel, J. T. Starczewski, and C. Napoli. Handwriting recognition with extraction of letter fragments. In L. Rutkowski, M. Korytkowski, R. Scherer, R. Tadeusiewicz, L. A. Zadeh, and J. M. Zurada, editors, Artificial Intelligence and Soft Computing, pages 183–192, Cham, 2017. Springer International Publishing.
• [11] M. Wróbel, J. T. Starczewski, and C. Napoli. Grouping handwritten letter strokes using a fuzzy decision tree. In L. Rutkowski, R. Scherer, M. Korytkowski, W. Pedrycz, R. Tadeusiewicz, and J. M. Zurada, editors, Artificial Intelligence and Soft Computing, pages 103–113, Cham, 2020. Springer International Publishing.
• [12] M. Wróbel, J. T. Starczewski, K. Nieszporek, P. Opiełka, and A. Kaźmierczak. A greedy algorithm for extraction of handwritten strokes. In L. Rutkowski, R. Scherer, M. Korytkowski, W. Pedrycz, R. Tadeusiewicz, and J. M. Zurada, editors, Artificial Intelligence and Soft Computing, pages 464–473, Cham, 2019. Springer International Publishing.
• [13] M. Zalasiński, K. Łapa, K. Cpałka, K. Przybyszewski, and G. G. Yen. On-line signature partitioning using a population based algorithm. Journal of Artificial Intelligence and Soft Computing Research, 10(1):5–13, 2019.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).