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Recognition of handwritten Latin characters with diacritics using CNN

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Języki publikacji
EN
Abstrakty
EN
Convolutional Neural Networks (CNN) have achieved huge popularity in solving problems in image analysis and in text recognition. In this work, we assess the effectiveness of CNN-based architectures where a network is trained in recognizing handwritten characters based on Latin script. European languages such as Dutch, French, German, etc., use different variants of the Latin script, so in the conducted research, the Latin alphabet was extended by certain characters with diacritics used in Polish language. To evaluate the recognition results under the same conditions, a handwritten Latin dataset was also developed. The proposed CNN architecture produced an accuracy of 96% for the extended character set. This is comparable to state-of-the-art results found in the domain of identifying handwritten characters. The presented approach extends the usage of CNN-based recognition to different variants of the Latin characters and shows it can be successfully used for a set of languages based on that script. It seems to be an effective technique for a set of languages written using the Latin script.
Rocznik
Strony
art. no. e136210
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
  • Lublin University of Technology, ul. Nadbystrzycka 38D, 20-618 Lublin, Poland
  • Systems Research Institute Polish Academy of Sciences, ul. Newelska 6, 01-447 Warsaw, Poland
  • Lublin University of Technology, ul. Nadbystrzycka 38D, 20-618 Lublin, Poland
autor
  • Lublin University of Technology, ul. Nadbystrzycka 38D, 20-618 Lublin, Poland
  • Lublin University of Technology, ul. Nadbystrzycka 38D, 20-618 Lublin, Poland
  • Lublin University of Technology, ul. Nadbystrzycka 38D, 20-618 Lublin, Poland
  • Lublin University of Technology, ul. Nadbystrzycka 38D, 20-618 Lublin, Poland
  • Lublin University of Technology, ul. Nadbystrzycka 38D, 20-618 Lublin, Poland
Bibliografia
  • [1] E. Lukasik and T. Zientarski, “Comparative analysis of selected programs for optical text recognition”, J. Comput. Sci. Inst. 7, 191‒194 (2018).
  • [2] P. Kusaj, M. Kosyra, and M. Charytanowicz, “Web-Page Classification Based on Wikipedia Structure. Recent Developments” in Mathematics and Informatics, Contemporary Mathematics and Computer Science 2, Part II, A. Zapała (red.), pp. 89‒102, Wydawnictwo KUL, 2016.
  • [3] D. Połap and M. Woźniak, “Flexible neural network architecture for handwritten signatures recognition”, Int. J. Electron. Telecommun. 62, 197–202 (2016).
  • [4] M. Milosz and J. Gazda, “Effectiveness of artificial neural networks in recognising handwriting characters”, J. Comput. Sci. Inst. 7, 210‒214 (2018).
  • [5] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition”. Proc. IEEE 86(11), 2278‒2324 (1998).
  • [6] A. Pal and D. Singh, “Handwritten English character recognition using neural network”, Int. J. Comput. Sci. Commun. 1(2), 141‒144 (2010).
  • [7] B.K. Verma, “Handwritten Hindi character recognition using multilayer perceptron and radial basis function neural network”, IEEE International Conference on Neural Network 4, 2111‒2115 (1995).
  • [8] D. Singh, S.K. Singh, and M. Dutta, “Hand written character recognition using twelve directional feature input and neural network”, Int. J. Comput. Appl. 1(3), 94‒98 (2010).
  • [9] Y. Perwej and A. Chatirvedi, “Neural networks for handwritten English alphabet recognition”, Int. J. Comput. Appl. 20(7), 1–5 (2011).
  • [10] J. Pradeep, E. Srinivasan, and S. Himavathi, “Neural network based handwritten character recognition system without feature extraction”, 2011 International Conference on Computer, Communication and Electrical Technology (ICCCET), Tamilnadu, 2011, pp. 40‒44.
  • [11] A.M. Obaid, H.M. El Bakry, M.A. Elodusuky, and A.I. Shehab, “Handwritten text recognition system based on neural network”, Int. J. Adv. Res. Comput. Sci. Technol. 4(1), 72‒77 (2016).
  • [12] V. Lebedev and V. Lempitsky. “Speeding-up convolutional neural networks: A survey”, Bull. Pol. Ac.: Tech. 66(6), 799‒810 (2018).
  • [13] D. Firmani, P. Merialdo, E. Nieddu, and S. Scardapane, “In codice ratio: OCR of handwritten Latin documents using deep convolutional networks”, in AI* CH@ AI* IA, 2017, pp. 9‒16.
  • [14] F.P. Such, D. Peri, F. Brockler, P. Hutkowski, and R. Ptucha. “Fully convolutional networks for handwriting recognition”. In: 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), IEEE, 2018, pp. 86‒91.
  • [15] P. Grother, “NIST special database 19 handprinted forms and characters database”, National Institute of Standards and Technology, Tech. Rep., 1995.
  • [16] M. Lutf, X. You, Y. Cheung, and C.L.P. Chen, “Arabic font recognition based on diacritics features”, Pattern Recognit. 47, 672–684 (2014).
  • [17] K.E. Gajoui, F.A. Allah, and M. Oumsis, “Diacritical Language OCR based on neural network: Case of Amazigh language”. Procedia Comput. Sci. 73, 298‒305 (2015).
  • [18] J. Náplava, M. Straka, P. Straňák, and J. Hajič, “Diacritics Restoration Using Neural Networks”, Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC), 2018.
  • [19] D. Grzelak, K. Podlaski, and G. Wiatrowski, “Analyze the effectiveness of an algorithm for identifying Polish characters in handwriting based on neural machine learning technologies”, Journal of King Saud University – Computer and Information Sciences, 2019, doi: 10.1016/j.jksuci.2019.08.001.
  • [20] G. Cohen, S. Afshar, J. Tapson, and A. van Schaik, ”EMNIST: an extension of MNIST to handwritten letters”. Retrieved from: http://arxiv.org/abs/1702.05373, 2017.
  • [21] M. Tokovarov, M. Kaczorowska, and M. Milosz, “Development of Extensive Polish Handwritten Characters Database for Text Recognition Research”, Adv. Sci. Technol. Res. J. 14(3), 30–38 (2020), doi:10.12913/22998624/122567.
  • [22] M. Charytanowicz and P. Kulczycki, “An Image Analysis Algorithm for Soil Structure Identification“; in: Intelligent Systems’2014, pp. 681‒692, D. Filev, J. Jablkowski, J. Kacprzyk, I. Popchev, L. Rutkowski, V. Sgurev, E. Sotirova, P. Szynkarczyk, S. Zadrozny (eds.), Springer, Berlin, 2014.
  • [23] The Polish Handwritten Characters Database, [Online]. https:// cs.pollub.pl/phcd/?lang=en.
  • [24] D.P. Kingma and J.L. Ba, “Adam: A method for stochastic optimization”. arXiv:1412.6980v9, 2014.
  • [25] M. Abadi et al., “Tensorflow: A system for large-scale machine learning,” in 12th Symposium on Operating Systems Design and Implementation, 2016, pp. 265‒283.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-455de96a-4fc1-4f10-8a97-8f02672277ec
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