TY - GEN
T1 - Belge İmgeleri Siniflandirma İçin Evrişimsel Sinir Aǧi Modellerinin Karşilaştirilmasi
AU - Yaman, Doggucan
AU - Eyiokur, Fevziye Irem
AU - Ekenel, Hazim Kemal
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/27
Y1 - 2017/6/27
N2 - Despite the increase in digitization, the use of documents is still very common today. It is essential that these documents are correctly labeled and classified for their need to be archived in an accessible manner. In this study, we used state-of-the-art convolutional neural network models to satisfy this need. Convolutional Neural Networks achieve high performance compared to alternative methods in the field of classification, due to the strong and rich features they can learn from large data through deep architecture. For the experiments, we have used a dataset containing 400,000 images of 16 different document classes. The state-of-the-art deep learning models have been fine-tuned and compared in detail. VGG-16 architecture has achieved the best performance on this dataset with 90.93% correct classification rate.
AB - Despite the increase in digitization, the use of documents is still very common today. It is essential that these documents are correctly labeled and classified for their need to be archived in an accessible manner. In this study, we used state-of-the-art convolutional neural network models to satisfy this need. Convolutional Neural Networks achieve high performance compared to alternative methods in the field of classification, due to the strong and rich features they can learn from large data through deep architecture. For the experiments, we have used a dataset containing 400,000 images of 16 different document classes. The state-of-the-art deep learning models have been fine-tuned and compared in detail. VGG-16 architecture has achieved the best performance on this dataset with 90.93% correct classification rate.
KW - convolutional neural network
KW - deep learning
KW - document classification
UR - http://www.scopus.com/inward/record.url?scp=85026327901&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85026327901&partnerID=8YFLogxK
U2 - 10.1109/SIU.2017.7960562
DO - 10.1109/SIU.2017.7960562
M3 - Conference contribution
AN - SCOPUS:85026327901
T3 - 2017 25th Signal Processing and Communications Applications Conference, SIU 2017
BT - 2017 25th Signal Processing and Communications Applications Conference, SIU 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th Signal Processing and Communications Applications Conference, SIU 2017
Y2 - 15 May 2017 through 18 May 2017
ER -