https://oldena.lpnu.ua/handle/ntb/52434| Title: | Braille Character Recognition Based on Neural Networks |
| Authors: | Smelyakov, Kirill Yeremenko, Dmytro Polezhai, Vitalii Sakhon, Anton Chupryna, Anastasiya |
| Affiliation: | Kharkiv National University of Radio Electronics |
| Bibliographic description (Ukraine): | Braille Character Recognition Based on Neural Networks / Kirill Smelyakov, Dmytro Yeremenko, Vitalii Polezhai, Anton Sakhon, Anastasiya Chupryna // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 509–513. — (Machine Vision and Pattern Recognition). |
| Bibliographic description (International): | Braille Character Recognition Based on Neural Networks / Kirill Smelyakov, Dmytro Yeremenko, Vitalii Polezhai, Anton Sakhon, Anastasiya Chupryna // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 509–513. — (Machine Vision and Pattern Recognition). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | alphabet artificial neural network Braille character image processing image recognition |
| Number of pages: | 5 |
| Page range: | 509-513 |
| Start page: | 509 |
| End page: | 513 |
| Abstract: | Braille is the most popular system used for interaction between visually-impaired and sighted people using tactile means. Optical Braille character recognition (OBCR) includes two main steps: Braille cells’ recognition (image acquisition, preprocessing, Braille dots’ recognition, Braille cells’ recognition and segmentation) and Braille cells’ transcription to corresponding natural language characters. System example has been created using image processing methods and artificial neural networks approach. These methods allow to achieve high speed and recognition accuracy level. System can adapt to factors like quality of input patterns and differences between them dynamically. In this paper, artificial neural network is developed to identify letter's images of Cyrillic alphabet in Braille representation system. Network will be trained and tested for identifying of scanned Cyrillic letters in Braille. Some of the letters are noised with some type of noise to simulate the real-world environment. |
| URI: | https://ena.lpnu.ua/handle/ntb/52434 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| URL for reference material: | http://www.who.int/mediacentre/factsheets/fs282/en/ |
| References (Ukraine): | [1] W. H. Organization. Visual impairment and blindness. 2017. Available: http://www.who.int/mediacentre/factsheets/fs282/en/ [2] A. Al-Salman, A. El-Zaart, S. Al-Salman, and A. Gumaei, “A novel approach for Braille images segmentation,” Multimedia Computing and Systems (ICMCS), International Conference on. IEEE, pp. 190–195, 2012. [3] T. Shreekanth, and V. Udayashankara, ”A review on software algorithms for optical recognition of embossed Braille characters,” International Journal of Computer Applications, vol. 81, no.3, pp. 25-35, 2013. [4] A. Al-Salman, A. El-Zaart, Y. Al-Suhaibani, K. Al-Hokail, and A. A. Al-Qabbany, “An efficient Braille cells recognition,” Wireless Communications Networking and Mobile Computing (WiCOM), 6th International Conference on. IEEE, pp. 1–4, 2013. [5] M. Wajid, M. W. Abdullah, and O. Farooq, “Imprinted Braille character pattern recognition using image processing techniques,” Image Information Processing (ICIIP), International Conference on IEEE, pp. 1–5, 2011. [6] I. Aleksander, and H. Morton, An introduction to neural computing”, 2nd edition. London: International Thomson Computer Press. 1995. [7] J. M. Zurada, Introduction to Artificial Neural Networks, 2nd edition. Published by Jaico Publishing House, India, 1996. [8] S. S. Haykin, Neural Networks: A Comprehensive foundation. Prentice Hall, 1999. [9] E. V. Bodyanskiy, and O. G. Rudenko. Artificial neural networks: architectures, обучение, applications. Kharkiv, «Teletech», 2004. [10] G. Morgavi, and M. Morando, “A neural network hybrid model for an optical Braille recognitor,” International Conference on Signal, Speech and Image Processing (ICOSSIP), 2014. [11] J. Li, and Y. Xiaoguang, “Optical Braille Character Recognition with Support-Vector Machine Classifier,” International Conference on Computer Application and System Modeling (ICCASM), 2010. [12] Zhang Namba, ”Cellular Neural Network for Associative Memory and Its Application to Braille Image Recognition”, International Joint Conference on Neural Networks, BC, Canada, pp. 2409 – 2414, 2006. [13] Java Neural Network Framework Neuroph Manual, v2.93, December 2017. |
| References (International): | [1] W. H. Organization. Visual impairment and blindness. 2017. Available: http://www.who.int/mediacentre/factsheets/fs282/en/ [2] A. Al-Salman, A. El-Zaart, S. Al-Salman, and A. Gumaei, "A novel approach for Braille images segmentation," Multimedia Computing and Systems (ICMCS), International Conference on. IEEE, pp. 190–195, 2012. [3] T. Shreekanth, and V. Udayashankara, "A review on software algorithms for optical recognition of embossed Braille characters," International Journal of Computer Applications, vol. 81, no.3, pp. 25-35, 2013. [4] A. Al-Salman, A. El-Zaart, Y. Al-Suhaibani, K. Al-Hokail, and A. A. Al-Qabbany, "An efficient Braille cells recognition," Wireless Communications Networking and Mobile Computing (WiCOM), 6th International Conference on. IEEE, pp. 1–4, 2013. [5] M. Wajid, M. W. Abdullah, and O. Farooq, "Imprinted Braille character pattern recognition using image processing techniques," Image Information Processing (ICIIP), International Conference on IEEE, pp. 1–5, 2011. [6] I. Aleksander, and H. Morton, An introduction to neural computing", 2nd edition. London: International Thomson Computer Press. 1995. [7] J. M. Zurada, Introduction to Artificial Neural Networks, 2nd edition. Published by Jaico Publishing House, India, 1996. [8] S. S. Haykin, Neural Networks: A Comprehensive foundation. Prentice Hall, 1999. [9] E. V. Bodyanskiy, and O. G. Rudenko. Artificial neural networks: architectures, obuchenie, applications. Kharkiv, "Teletech", 2004. [10] G. Morgavi, and M. Morando, "A neural network hybrid model for an optical Braille recognitor," International Conference on Signal, Speech and Image Processing (ICOSSIP), 2014. [11] J. Li, and Y. Xiaoguang, "Optical Braille Character Recognition with Support-Vector Machine Classifier," International Conference on Computer Application and System Modeling (ICCASM), 2010. [12] Zhang Namba, "Cellular Neural Network for Associative Memory and Its Application to Braille Image Recognition", International Joint Conference on Neural Networks, BC, Canada, pp. 2409 – 2414, 2006. [13] Java Neural Network Framework Neuroph Manual, v2.93, December 2017. |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2018_Smelyakov_K-Braille_Character_Recognition_509-513.pdf | 353.6 kB | Adobe PDF | View/Open | |
| 2018_Smelyakov_K-Braille_Character_Recognition_509-513__COVER.png | 1.34 MB | image/png | View/Open |
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