KaydedilenlerDerginizi ekleyin

Dergiler / International Journal of Engineering Technologies / 2026 / Cilt: 5 Sayı: 1

Handwritten Character Recognition by using Convolutional Deep Neural Network; Review

Baki Koyuncu
Hakan Koyuncu(ISTANBUL GELISIM UNIVERSITY)0000-0002-8444-1094
Sayfa
1–5
Yayın Tarihi
2019-03-29
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

This model, initially, learned a useful set of support by using core and local receptive areas and then a densely connected network layers are employed for the discernment task.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Handwritten character recognition is an important domain of research with implementation in varied fields.
📊
Temel Bulgular & Veri:Past and recent works in this field focus on diverse languages to utilize the character recognition in automated data-entry applications.
💡
Sonuç & Katkı:This model, initially, learned a useful set of support by using core and local receptive areas and then a densely connected network layers are employed for the discernment task.
🔬Tür:Literatür & Derleme Çalışması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Handwritten Character Recognition · Deep Neural Network (DNN) · Deep Convolutional Neural Network (DCNN)

Abstract

Handwritten character recognition is an important domain of research with implementation in varied fields. Past and recent works in this field focus on diverse languages to utilize the character recognition in automated data-entry applications. Deep Neural network studies recognize the individual characters in the form images. The reliance of each recognition, which is provided by the neural network as part of the ranking result, is one of the things used to customize the implementation to the request of the client. Convolutional Deep neural network model is reviewed to recognize the handwritten characters in this study. This model, initially, learned a useful set of support by using core and local receptive areas and then a densely connected network layers are employed for the discernment task.

🔖

Bu Makaleye Atıf Yap

Koyuncu, B., Koyuncu, H. (2019). Handwritten Character Recognition by using Convolutional Deep Neural Network; Review. International Journal of Engineering Technologies, Cilt: 5 Sayı: 1, 1–5. https://doi.org/10.19072/ijet.528775

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (18)

  1. R. Vaidya, D. Trivedi, S. Satra, M. Pimpale,“Handwritten Character Recognition Using DeepLearning”. Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 772-775, 2018
  2. G. S. Budhi and R. Adipranata, “Handwritten Javanese Character Recognition Using Several Artificial Neural Network Methods”, J.ICT Res. Appl., vol. 8, no. 3, pp. 195–212, 2015
  3. A. Rajavelu, M.T. Musavi, and M.V. Shirvaikar, “A neural network approach to character recognition”, Neural Netw., vol. 2, no. 5, pp. 387– 393, 1989.
  4. S. Mori, C. Y. Suen, and K. Yamamoto, “Historical review of OCR research and development,” Proc. IEEE, vol. 80, no. 7, pp. 1029 –1058, 1992
  5. J. Pradeep, E. Srinivasan and S. Himavathi. “Neural Network based Handwritten Character Recognition system without feature extraction“, International Conference on Computer, Communication and Electrical Technology ICCCET 2011
  6. K. Gurney, “An introduction to neural networks”, UCL Press, 1997
  7. Y. LeCun, Y. Bengio and G. Hinton, "Deep learning", Nature, Vol. 521, pp. 436-444, 2015
  8. Y. Liang, J. Wang, S. Zhou, Y. Gong, and N. Zheng, “Incorporating image priors with deep convolutional neural networks for image super resolution”, Neurocomputing, Vol. 194, pp. 340- 347, 2016
  9. R. Nijhawan, H. Sharma, H. Sahni, and A. Batra, “A deep learning hybrid CNN framework approach for vegetation cover mapping using deep features”, 13th International Conference on Signal Image Technology & Internet-Based Systems (SITIS), pp. 192-196, 2017
  10. K. Fukushima, “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position”, Biological Cybernetics, vol. 36, no. 4, pp. 193–202, 1980
  11. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Advances in neural information processing systems, pp. 1097–1105, 2012
  12. C. Farabet, C. Couprie, L. Najman, and Y. LeCun, “Learning hierarchical features for scene labeling”, IEEE Trans. Pattern Anal. Mach. Intel., Vol. 35, no. 8, pp. 1915–1929, 2013
  13. O. Vinyals, A. Toshev, S. Bengio, and D. Ethan, “Show and tell: A neural image caption generator”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3156–3164, 2015
  14. D. C. Ciresan, U. Meier, J. Masci, L. Maria Gambardella, and J. Schmidhuber, “Flexible, high performance convolutional neural networks for image classification”, Proceedings in 22nd International Joint Conference on Artificial Intelligence, Vol. 22, pp. 1237-1242, 2011
  15. E. Kussul and T. Baidyk, “Improved method of handwritten digit recognition tested on MNIST database”, Image Vis. Compute., vol. 22, no. 12, pp. 971–981, 2004
  16. W. Lu, Z. Li, B. Shi.” Handwritten Digits Recognition with Neural Networks and Fuzzy Logic”, IEEE International Conference on Neural Networks, Vol. 3, pp.1389-1392, 1995
  17. P. Banumathi, G. M. Nasira, “Handwritten Tamil Character Recognition using Artificial Neural Networks”, International Conference on Process Automation, Control and Computing, 2011
  18. B. V. S. Murthy,” Handwriting Recognition Using Supervised Neural Networks”, International Joint Conference on Neural Networks, 1999