El Yazısı Karakterlerin Tanınmasında Derin Sinir Ağlarının Etkinliği Kısıtlı ve Geniş Sınıf Yapılarının Performans Analizi

References

1. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, Nov. 1998.
2. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems, vol. 25, 2012, pp. 1097–1105.
3. M. Kaya, “Feature fusion-based ensemble CNN learning optimization for automated detection of pediatric pneumonia,” Biomed. Signal Process. Control, vol. 87, p. 105472, 2024.
4. M. Kaya and Y. Çetın-Kaya, “A novel deep learning architecture optimization for multiclass classification of Alzheimer’s disease level,” IEEE Access, vol. 12, pp. 46562–46581, 2024.
5. Gonzalez, R. C., & Woods, R. E. (2018). Digital Image Processing. Pearson.
6. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature.
7. Barbedo, J. G. A. (2013). Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus.
8. M. Kaya, S. Ulutürk, Y. Çetin Kaya, O. Altıntaş ve B. Turan "Optimization of Several Deep CNN Models for Waste Classification," Sakarya Üniversitesi Bilgisayar Bilimleri ve Mühendisliği Dergisi, http://saucis.sakarya.edu.tr/en/pub/issue/79575/1257100
9. Cohen, G., Afshar, S., Tapson, J., & van Schaik, A. (2017). EMNIST: Extending MNIST to handwritten letters.
10. de Campos, T. E., Babu, B. R., & Varma, M. (2009). Character recognition in natural images.
11. Clanuwat, T., Bober-Irizar, M., Kitamoto, A., Lamb, A., Yamamoto, K., & Ha, D. (2018). Deep Learning for Classical Japanese Literature.
12. Jindal, A., Dua, M., & Kumar, M. (2018). Handwritten Arabic character recognition using convolutional neural network.
13. Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal Loss for Dense Object Detection. ICCV.
14. Buda, M., Maki, A., & Mazurowski, M. A. (2018). A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks.
15. Afzal, M. Z., Kölsch, A., Ahmed, S., & Liwicki, M. (2019). Deep learning based baseline detection in historical documents.
16. Jaderberg, M., Simonyan, K., Vedaldi, A., & Zisserman, A. (2014). Synthetic data and artificial neural networks for natural scene text recognition.
17. Miyazaki, K., & Kitamoto, A. (2019). Improving character recognition in Kuzushiji using ResNets.
18. Alginahi, Y. M., Alabrah, A., & Alotaibi, M. (2020). Arabic handwritten character recognition using hybrid deep neural networks
19. S. Mann, “Handwritten English Characters and Digits,” Kaggle, 2023. [Online]. Available: https://www.kaggle.com/datasets/sujaymann/handwritten-english-characters-and-digits

Cited by

1 — Crossref — cited-by count (retrieved 2 Oct 2026, source)

Citation counts are shown with their source and retrieval date. They are not an Impact Factor or any journal-level metric.