Convolution Neural Network For Traditional Chinese .
rs and depth. Althoughhandwritten Chinese characters share similarity with TCC,there are major difference among the them. In terms ofrecognitions goal, handwritten Chinese characterrecognition aims to predict the label of every character,whereas in our project, our goal is to recognize TCCstyles, which makes the task easier in terms of number ofclasses. Another difference is that, handwritten Chinesecharacters are usually written with pen, which have verythin line width, whereas TCC characters are written withbrush and have significant features in the width of everyline.Many TCC recognition approaches focus on how toextract both the contour features and the width features.And different variations of HOG feature have been used.For example, Lu et al. [3] used a method of featureextraction called SC-HoG descriptor to represent the shapeof TCC characters. However, most of the approaches useK nearest neighbor as classifier to solve the problem. Andwe didn’t find examples of using CNN on TCCrecognition.stacking of two 3x3 convolutional layers is equivalent toone 5x5 convolutional layers in terms of the effectivereceptive field, but with fewer number of parameters andmore non-linearity activation layers. Between L9 and L9 ,we want to test the influence of putting additional layers inthe front versus in the back of the convolutional layers.We added 2 more convolutional layers in L11 and L11 models. And in L11 we add batch-normalization layersafter every convolutional layer. The batch normalizationlayer perform normalization for each training mini-batch.In training process, it computes the mean and varianceover the mini-batch input and then normalize the input.And the scale and shift parameters can be learned duringthe training process. The equations are shown below.3. MethodsWe use architecture of CNN based on VGGnet model[4]. Since one of our goal in this project is to investigatethe influence of number of layers and number of filters onthe performance of the classification. We built eightdifferent CNN models. The difference of the configurationof the models are the number of layers and number offilters. In every model, we use filters of 3x3 with stride of1 and zero-padding of 1 to preserve input height andwidth, and use max-pooling with 2x2 filters and stride of 2for down-sampling. Table 2 shows the detail layout ofeach model. Our models are built using Keras library [5]with theano backend [6].Our baseline model is L6, which has 3 convolutionallayers followed by 3 fully-connected layers. We thenincrease one more convolutional layer in L7 and L7 models. The difference between L7 and L7 is that weincrease the number of filters in L7 model. In L9 andL9 model, we add two more convolutional layersbetween non-linearity and max-pooling. As is described by[4], we stack two convolutional layers with 3x3 filters. The(1)(2)(3)(4)According to Ioffe and Szegedy[7], batch normalizationis able to stabilize the learning process, and allow using ofhigher learning rate. These advantages of batchnormalization help us a lot during the training of L11 , but12
also introduce some problems, which will be discussed inthe next section. In L13 model, we add one additionalconv-conv-max-pooling structure to make the networkdeeper.Although not shown in Table 1, we use non-linearityReLU activation after convolutional layers in every modelexcept L11 . In L11 , the ReLU layers are used afterbatch normalization layers. We use softmax function tocompute the loss. The equation of softmax function isshown below.dataset. We collected 15000 TCC characters, with 3000characters for each style. We use 12000 characters fortraining, and 1200 characters for validation, and 1800 fortest.We did preprocessing on our dataset. The datasetcontains JPEG format RGB images with variousdimensions. First, we imported the images into MATLABand computed the mean height and width of the images.The result is shown in Table 2.Table 2. Mean Height and Width of Images of DatasetStandard(5)
script. Fig. 1 shows examples of the same TCC characters in all five major styles. Figure 1. Standard script, clerical script, seal script, cursive script, and semi-cursive script (From left to right) The standard script is used in daily life. The clerical script is similar to stan
“separable convolution” in deep learning frameworks such as TensorFlow and Keras, consists in a depthwise convolution, i.e. a spatial convolution performed independently over each channel of an input, followed by a pointwise convolution, i.e. a 1x1 convolution, projecting the channels o
Performance comparison of adaptive shrinkage convolution neural network and conven-tional convolutional network. Model AUC ACC F1-Score 3-layer convolutional neural network 97.26% 92.57% 94.76% 6-layer convolutional neural network 98.74% 95.15% 95.61% 3-layer adaptive shrinkage convolution neural network 99.23% 95.28% 96.29% 4.5.2.
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operations described in this section are for 2D-CNN, similar operations can also be performed for three-dimensional (3D)-CNN. Convolution layer A convolution layer is a fundamental component of the CNN architecture that performs feature extraction, which typically Convolution Convolution is a specialized type of linear operation used for
per, we propose a Context-adaptive Convolution Network (CaC-Net) to predict a spatially-varying feature weighting vector for each spatial loca-tion of the semantic feature maps. In CaC-Net, a set of context-adaptive convolution kernels are predicted from the global contextual information in a parameter-e cient manner. When used for convolution .
the methods described above use regular 3D convolution to process hyperspectral image, and there are many similar methods, such as [31] and [32]. Different from 2D convolution, a regular 3D convolution is performed by convoluting 3D kernel and feature map. It results in a significant increase in network parameters. Considering this shortcoming .
such as iterative tomographic reconstruction, can also depend on the implementation of dense space-varying convolution. Wh ile space-invariant convolution can be efficiently implemented with the Fast Fourier Transform (FFT), this approach does not work for space-varying operators. So direct convolution is often the
I f g is also called the generalized product of f and g. I The definition of convolution of two functions also holds in the case that one of the functions is a generalized function, like Dirac's delta. Convolution of two functions. Example Find the convolution of f(t) e t and g(t) sin(t).
