Hybrid handwriting character recognition with transfer deep learning [Aktarimli Derin Ö?renme ile Hibrit El Yazisi Karakter Tanima]
Küçük Resim Yok
Tarih
2019
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Handwriting character recognition is useful and important in terms of allowing correct recognition and interpretation of all characters such as handwritten letters, numbers and figures. Deep convolutional neural networks have been used frequently for computer vision in recent years due to high performance in image processing, feature extraction and classification. A lot of sample data, processing power and time are needed to train CNNs. Transfer learning enables us to obtain specific CNNs for the classes we want by minimizing these needs In this work, firstly, different CNN models are trained with transfer learning by using NIST19 dataset with handwritten characters, and then a hybrid model is created by evaluating the results of each CNN and revealing the best value. As a result of the experiment carried out on the test data set, it is observed that a performance increase of 1.1% is achieved with the created model. © 2019 IEEE.
Açıklama
27th Signal Processing and Communications Applications Conference, SIU 2019 -- 24 April 2019 through 26 April 2019 -- -- 151073
Anahtar Kelimeler
Convolutional Neural Network, Deep Learning, Handwriting Character Recognition, Transfer Learning
Kaynak
27th Signal Processing and Communications Applications Conference, SIU 2019
WoS Q Değeri
Scopus Q Değeri
N/A
