Title

Recognition of handwritten Latin characters with diacritics using CNN

Journal title

Bulletin of the Polish Academy of Sciences Technical Sciences

Yearbook

2021

Volume

69

Issue

No. 1

Authors

Lukasik, Edyta ; Charytanowicz, Malgorzata ; Milosz, Marek ; Tokovarov, Michail ; Kaczorowska, Monika ; Czerwinski, Dariusz ; Zientarski, Tomasz

Keywords

handwritten documents ; diacritics ; neural networks ; character recognition ; deep learning

Divisions of PAS

Nauki Techniczne

Coverage

e136210

Bibliography

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.

Date

26.01.2021

Type

Article

Identifier

DOI: 10.24425/bpasts.2020.136210

Source

Bulletin of the Polish Academy of Sciences: Technical Sciences; 2021; 69; No. 1; e136210