Segmentation-free writer identification based on convolutional neural network

2020 ◽  
Vol 85 ◽  
pp. 106707
Author(s):  
Parveen Kumar ◽  
Ambalika Sharma
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Writer Identification

scholarly journals Writer Identification with Hybrid Model using Hybrid HMM and ANN

2019 ◽  
Vol 8 (3) ◽  
pp. 1656-1661
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Hybrid Model ◽  
Optimum Number ◽  
Multi Layer Perceptron ◽  
Writer Identification ◽  
Mlp Model

In this paper, writer identification is performed with three models, namely, HMMBW, HMMMLP and HMMCNN. The features are extracted from the HMM and are classified using Baum Welch algorithm (BW), Multi layer perceptron (MLP) model and Convolutional neural network (CNN) model. A dataset, namely, VTU-WRITER dataset is created for the experiential purpose and the performance of the models were tested. The test train ratio was varied to derive its relation to accuracy. Also the number of states was varied to determine the optimum number of states to be considered in the HMM model. Finally the performance of all the three models is compared

scholarly journals Text-independent writer identification using convolutional neural network

2019 ◽  
Vol 121 ◽  
pp. 104-112 ◽  
Author(s):  
Hung Tuan Nguyen ◽  
Cuong Tuan Nguyen ◽  
Takeya Ino ◽  
Bipin Indurkhya ◽  
Masaki Nakagawa
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Writer Identification

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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