# Feature Extraction and Identification of Calligraphy Style Based on Dual Channel Convolution Network

Authors: Ziyan Wang
Journal: Security and Communication Networks
 (2022)

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## Abstract

To improve the effect of calligraphy style feature extraction and identification, this study proposes a calligraphy style feature extraction and identification technology based on two-channel convolutional neural network and constructs an intelligent calligraphy style feature extraction and identification system. Moreover, this paper improves the C3D network model and retains 2 fully connected layers. In addition, by extracting the outline skeleton and stroke features of calligraphy characters, this paper calculates the feature weight and authenticity determination function and constructs an authenticity identification system. The experimental study shows that the calligraphy style feature extraction and identification system based on the dual-channel convolutional neural network proposed in this paper has a good performance in calligraphy style feature extraction and identification.


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## Body

## 1. Introduction

The digitized historical calligraphy works, books, and signatures contain tens of thousands of individual calligraphy characters, covering the original calligraphy works or the photocopies of various historical periods and famous calligraphers. These calligraphic works are displayedin the CADAL portal. Users can search these calligraphic works based on metadata information such as the title of the work and the name of the calligrapher. At the same time, some calligraphy characters can also be retrieved by content-based methods by inputting or drawing a calligraphy character image. However, for digital calligraphy works with a large amount of data, the original outline-based calligraphy character retrieval technology has gradually been unable to meet the needs of user applications because it takes too long. At the same time, in the actual service, the calligraphy character database needs to be continuously supplemented. When adding new calligraphy characters to the calligraphy character database, in order to obtain complete metadata, each calligraphy character needs to be manually identified and entered into the database by the administrator. This is a very time-consuming and labor-intensive job. In addition, with the continuous increase of digitized calligraphy works, users also put forward new demands for better use of these calligraphy characters. There are many users who wish to use the words of a famous calligrapher in these calligraphy resources to inscribe their study or company. However, among those characters specified by the user, there may be some characters that are not present in the existing works of the individual specified calligraphers. There is a need for a technique that can generate such calligraphic characters.When identifying calligraphy samples through CNN, we must first extract the features of the image. The particularity of calligraphy samples is reflected in two aspects: the writing background is simple, usually black and white. The various writing styles are not completely unique, but there is a mutual reference. Moreover, there are many similar properties in the features. Before the calligraphy font is recognized, a good feature extractor is needed to extract the features of the samples [1]. In the Caffe neural network framework, the LMDB data storage format is used to store images, so it is necessary to format the training samples and test samples. The convolutional neural network is mainly composed of three parts: a feature extraction layer, a fully connected layer, and a classifier [2]. Among them, the feature extraction layer performs feature extraction on the input samples, and the obtained feature images are subjected to multiple visual enlargement processing to retain the representative image feature information, and then the features are vectorized through the fully connected layer. Finally, the retained eigenvectors are probability normalized by the classifier, and the categories are divided according to the principle of maximum probability [3].This paper proposes a calligraphy style feature extraction and identification technology based on two-channel convolutional neural network, constructs an intelligent calligraphy style feature extraction and identification system, and improves the effect of calligraphy style feature extraction and identification.


## 2. Related Work

Reference [4] proposed a calligraphic character retrieval method based on the similarity of calligraphic character outlines. This method was used to test 20 calligraphic characters, and the precision was close to 90% under the 90% recall rate. Reference [5] proposed a method for retrieving calligraphy characters based on the skeleton features of calligraphy characters. Literature [6] proposes a fast multilayer retrieval method for calligraphy characters, which improves the retrieval speed through two-layer calligraphy character retrieval. The full-rate and precision rate remain basically unchanged. Reference [7] proposes a SC.HoG feature to describe calligraphy characters, which expresses the position information of a certain contour point of calligraphy characters and the distribution information of contour points around the contour point, so as to perform shape-based retrieval for calligraphy characters. Reference [8] first performs pruning processing, and filters out calligraphic character images that are impossible to be similar to the calligraphic character image to be retrieved by comparing the complexity of calligraphy characters, stroke density, and other characteristics. Dimensional calligraphy image feature data is used for retrieval, and PK-tree is used to improve retrieval speed. Reference [9] proposed an ancient book content retrieval method based on visual similarity. The above-mentioned content-based calligraphy character retrieval research shows that the extracted calligraphy characters have high feature dimensions. Therefore, with the continuous increase of calligraphy characters in the database, in the retrieval of large data calligraphy characters, when comparing and calculating the similarity, it still consumes a lot of time, and the retrieval speed becomes slower and slower, which cannot meet the needs of online services.Reference [10] proposes a high-dimensional calligraphic character index method based on hybrid distance-tree (HD-Tree) to speed up retrieval. Given a query calligraphic character image, the hybrid distance tree index is used to complete high-dimensional calligraphy characters, image query. Reference [11] proposed an interactive high-dimensional vector indexing method based on partial-distance-map (PDM). PDM mainly establishes the relationship between the concept of Chinese character semantic level and the underlying shape features through user feedback to improve the retrieval speed of the massive calligraphy character database.Literature [12] proposed a calligraphy character recognition method, which is based on the calligraphy character retrieval. First, the calligraphy character skeleton similarity retrieval method is used to retrieve the calligraphy in the database that is similar to the calligraphy character image to be recognized. Then, according to the semantic annotation of the retrieved similar calligraphic character images in the database, the recognition results are given. In the experiment, 300 calligraphy characters were tested and the recognition rate reached 96.3%. This method is based on retrieval, and the recognition time depends on the time required to retrieve similar calligraphic characters in the database. When the database is large, the time efficiency of this recognition method is very low. The polygon approximation method is to use polygon line segments to approximate the shape edge, and the minimum error is generally used to measure the quality of the approximation method used. Reference [13] proposed a shape polygon approximation method based on Hopfield neural network. Reference [14] proposed a relaxed iterative matching algorithm. This method uses the start and the end coordinates, length, and direction to describe stroke outline segments. A spline refers to a piecewise-defined polynomial parametric curve. For a given node vector, all splines of degree n form a vector space. A basis for this space is a B-spline of degree n. Reference [15] proposed a B-spline curve matching method with affine invariance. The commonly used scale spaces are: Gaussian scale space, wavelet scale space, and shape scale space. The salient feature points of the target refer to those feature points that still exist in the simplified representation by tracking the positions of the feature points at different scales to give a simplified form of the shape.The detection and description of image local features can be effectively used to identify objects. SIFT features are based on the principle that the key points of the local appearance on the object are independent of the size and rotation of the image. In addition, the tolerance of SIFT features to changes in light, noise, and viewing angle is also higher [16]. Based on the above advantages, the feature information of the image is relatively easy to extract. After the image feature database is established, it is easy to identify objects in the feature database, and the recognition rate is high. SIFT feature description also has a high detection rate for objects with partial occlusion, and only a small amount of SIFT image features can be used to calculate the position and orientation of key points. The SIFT method has high operating efficiency, and the SIFT feature library has a large amount of information, and the recognition speed is close to real-time operation, so the algorithm is suitable for efficient and accurate matching of massive data [17].The speeded-up robust features (SURF) operator is an improved algorithm proposed on the basis of maintaining the excellent performance of the SIFT operator. SURF solves the shortcomings of high computational complexity and time-consuming SIFT [18]. The extraction and its feature vector description have been improved, and the calculation speed has been greatly improved. SURF uses the Hessian matrix to stably obtain the extreme value of the image space. However, in the stage of finding the main direction, it completely depends on the gradient direction of the local pixel points, which may cause the main direction to be inaccurate [19], and because the feature vector extraction and matching are completely dependent on the above, the main direction is determined by the step and even a small deviation angle may cause the error of feature matching [20].


## 3. Two-Channel Convolutional Network Image Feature Extraction

The training idea of the convolutional neural network is to define a loss function, train the network through the existing samples, optimize the network parameters by using the back-propagation algorithm, and obtain the parameters of the network when the loss function is the smallest.The neural network defines the loss function for a single sample (x, y) as(1)JW,b;x,y=12hw,bx−y2.Here,W and b are the weight and bias parameters of the network respectively, hw,bx is the output of the sample x through the neural network, and y is the expected output value of the sample.For a training setx1,y1,⋯,xm,ym with m samples, the overall loss function is(2)JW,b=1m∑i=1mJW,b;xi,yi+λ2W2,=1M∑i=1m12hw,bxi−yi2+λ2∑l=1nl−1∑i=1Sl∑j=1Sl+1Wjil2.Here, the first term is the mean square error, the second term is the weight decay term, which is used to prevent overfitting,nl is the number of network layers, and sl is the number of neurons contained in the l-th layer network.Before training the network, the algorithm first randomly initializes the parameters of the weightW and bias b of the network and uses the gradient descent method to update W and b according to the following equations during training:(3)Wl=Wl−α∂∂WlJW,b,(4)bl=bl−α∂∂blJW,b.Among them,α is the learning rate, which determines whether the loss function can converge to the local minimum and the convergence speed. To update the parameters, it is necessary to obtain the partial derivatives of JW,b with respect to W and b, which uses the backpropagation algorithm, whose calculation formulas are shown in(5)∂∂WlJW,b=1m∑i=1m∂∂WlJW,b;xi,yi+λWl,(6)∂∂blJW,b=1m∑i=1m∂∂blJW,b;xi,yi.According to the chain method, it is derived as(7)∂JW,b;x,y∂Wl=∂JW,b;x,y∂zl+1T·∂zl+1∂Wl,=∂JW,b;x,y∂zl+1T·∂Wlal+bl∂Wl,=∂JW,b;x,y∂zl+1T·al.If the residual of thel-th layer network is δl=∂JW,b;x,y/∂zl, then the residual of the output layer (nl-th layer) is(8)δnl=∂JW,b;x,y∂znl=∂∂znl12hW,bx−y2,=∂∂znl12fznl−y2=fznl−y·f′znl,=anl−y·f′znl.Using mathematical induction, the residual of thel-th layer is obtained, and the expression is(9)δl=∂JW,b;x,y∂zl=wlTδl+1·f′zl.Therefore, the partial derivative ofJ(W, b) with respect to W is obtained as(10)∂JW,b;x,y∂Wl=δl+1T·al.Similarly, the partial derivative ofJ(W, b) with respect to b can be obtained as(11)∂JW,b;x,y∂bl=δl+1.After the partial derivatives ofJ(W, b) with respect to W and b are obtained, the parameters of the weight W and the bias b can be updated and optimized according to (3) and (4).Due to the introduction of convolutional layers and pooling layers, the training of CNN cannot be directly applied to the training method of DNN. There are several differences between them:(1)
There is no activation function in the pooling layer, which can be solved by setting the activation function of this layer tofz=z(2)
The pooling layer compresses the feature map during forward propagation(3)
DNN is a fully connected network, the output of the current layer is directly obtained by matrix multiplication, and the convolution layer is obtained by summing several matrices to obtain the current output(4)
For the convolutional layer, the operation method used by W is convolutionIn view of these differences, the backpropagation algorithm of CNN is discussed in the following two situations:(1)
Knowing theδl of the pooling layer, the δl−1 of the previous hidden layer is derived.The pooling layer first restoresδ(l) all submatrices to the size before pooling during backpropagation. If maximum pooling is used, the value of the δ(l) submatrix corresponding to the pooling area is placed in the position where the maximum value is taken during the forward propagation. If average pooling is used, the values of the pooled area corresponding to the δ(l) submatrix are averaged and put into the restored matrix. This restoring process is defined as upsample. The restoration process is further explained below with an example.We assume that the pooling window size is 22, the window sliding step is 2, and thek-th submatrix of δ(l) is δkl=2468, and the submatrix is restored to the size before pooling and becomes 0000024006800000. If it is maximum pooling, we assume that the maximum positions are the lower left, lower right, upper right, and lower right during forward propagation, then the restored matrix is 0000200406000008. If it is average pooling, the matrix after reduction is 0.50.5110.50.5111.51.5221.51.522.the value of the upper layer∂JW,b;x,y/∂akl−1 can be obtained by upsampling, then there are(12)δkl−1=∂JW,b;x,y∂zkl−1=∂JW,b;x,y∂akl−1∂akl−1∂zkl−1,upsampleδkl·f′zl−1.For the tensorδ(l−1), we have(13)δl−1=upsampleδl·f′zl−1.(2)
Knowing theδ(l) of the convolutional layer, it is deduced to δ(l−1) of the previous hidden layer.In DNN, the recurrence formula ofδ(l) and δ(l−1) is(14)δl−1=∂JW,b;x,y∂zl−1=∂JW,b;x,y∂zl∂zl∂zl=δl·∂zl∂zl−1.According to the forward propagation formula of the convolutional layer, we get(15)zl=al−1∗Wl+bl=fzl−1∗Wl+bl.Then, there are(16)δl−1=δl⋅∂zl∂zl−1=δl∗rot180Wl⋅f′zl−1.Among them,rot180() means to rotate 180 degrees.Now, given theδ(l) of the convolutional layer, the gradient of this layer W is(17)∂JW,b;x,y∂Wl=∂JW,b;x,y∂zl∂zl∂Wl=al−1∗δl.Sinceδ(l) is a three-dimensional tensor and b is a one-dimensional vector, the algorithm sums the submatrix items of δ(l) respectively, and the obtained one-dimensional error vector is the gradient of b. The calculation formula is(18)∂JW,b;x,y∂bl∑u,yδlu,y.The original LBP operator is defined in a fixed rectangular neighborhood of 3 × 3, which cannot meet the needs of textures of different sizes, so a circular LBP operator is generated. Compared with the original LBP operator, the circular LBP operator has two improvements: (1) the fixed neighborhood of 33 is extended to an arbitrary neighborhood; (2) the square neighborhood is no longer used, and the circular neighborhood is adopted. The circular LBP operator can be expressed asLBPP, R, where R is the radius of the circular neighborhood and P is the number of sampling points. Several common LBP operators are shown in Figure 1.Figure 1
Several common LBP operators.The calculation formula of the circular LBP operator is(19)LBPP,R=∑i=1psgi−gc2i−1.Here,su=1,u≥00,other, gc is the gray value of the pixel in the center of the neighborhood, and gi is the gray value of the pixel of the i-th sampling point in the neighborhood. To calculate the pixel gray value of the i-th sampling point, the coordinates xi,yi of the i-th sampling point are first calculated, and the calculation formula is(20)xi=xc+Rcos2πip,yi=yc+Rsin2πip.Here,(xc, yc) is the coordinate of the center pixel. It can be seen from Figure 1 that some sampling points on the boundary of the circular neighborhood cannot just fall within the pixel grid, and may also fall on the boundary. At this time, the pixel value of the sampling point is calculated by the bilinear interpolation method, and the calculation formula is(21)gxi,yi=1−xixigxi−1,yi−1gxi−1,yigxi,yi−1gxi,yi1−yiyi.In order to reduce the variety of binary patterns, Ojala proposes an equivalent pattern, which is also called uniform LBP. The main idea is that if the binary string corresponding to the mode does not jump from 0 to 1 or from 1 to 0 more than twice, then the mode is an equivalent mode, and all other modes are mixed modes, which are expressed as(22)ULBPp,R=∑i=1psgi−gc−sgi−1−gc.Here,g0=gp, and the mode satisfying U≤2 is the equivalent mode. The improved mode types are reduced from the original 2P to PP−1+2, which not only does not lose image information, but also reduces the impact of high-frequency noise.Three-dimensional convolution (3D convolution) performs convolution operations on both the spatial and temporal domains by extending the convolution kernel to the temporal domain. In this way, it can extract both spatial information of a single frame of image and temporal information between adjacent video frames. 3D convolution combines multiple consecutive video frame images into a cube and slides the 3D convolution kernel on the cube to perform the convolution operation. When performing the convolution operations on a sequence of video frames, the difference between 2D convolution and 3D convolution is as follows: after a 2D convolution, the generated feature map is an image, which loses the time information of the input data. However, through 3D convolution, the generated feature map is still a sequence of feature maps, which effectively captures the motion information of the target. The convolution process of 2D convolution and 3D convolution on the video frame sequence is shown in Figure2.Figure 2
Convolution to generate feature map: (a) feature map generated by 2D convolution of the video frame sequence; (b) the video frame sequence is subjected to 3D convolution to generate a feature map.
(a)(b)The network used in this section improves the original C3D network model and retains two fully connected layers, and its network structure is shown in Figure3. The improved C3D network contains 5 convolutional layers, 5 pooling layers, 2 fully connected layers, and 1 Softmax classifier. A ReLU layer is added after each convolutional layer and the first fully connected layer in the network. This is because there is a simple linear relationship between the layers of the network. By introducing a nonlinear function as an activation function, the algorithm increases the nonlinear relationship between the layers so that the network can fit complex functions. The formula of the ReLU function is shown in equation (5).(23)fx=max0,x.Figure 3
C3D network structure diagram.ResNet adopts a residual structure, as shown in Figure4(a). This module has two branches, one of which is the normal convolutional layer output and the other that directly connects the input to the output. The final output of the module is the arithmetic sum of the two branches, which is formulated as(24)Hx=Fx+x.Figure 4
Residual network: (a) residual structure; (b) residual module structure.
(a)(b)Here,H(x) represents the output of the entire structure, x is the input, and F(x) is the output of the convolutional layer. ResNet defines a residual function Fx=Hx−x. When all parameters in the F(x) branch are 0, Hx=x is the identity map. ResNet no longer learns the output of the entire structure, it is changed to learn the difference between the target value H(x) and the input x, and the training goal is to make the residual function F(x) approach 0.Obviously, fitting the residual function is easier than fitting the identity mapping function, and the training process of the network is optimized. The residual structure is implemented by connecting the forward neural network and the identity map without introducing additional parameters. This will not increase the computational complexity of the network, and the network training method still uses backpropagation. After adopting the residual structure, the deep neural network achieves the ideal classification effect. A typical residual module structure is shown in Figure4(b), which consists of stacking convolutional layers. Among them, 3×3×3 is the size of the convolution kernel of this layer, 128 is the number of convolution kernels, and /1×2×2 indicates that the convolution kernel moves with a step size of 1 in the time domain and 2 in the spatial domain.It is usually implemented by1×1 convolution, and the formula is(25)Hx=Fx+Wx.It should be noted that scheme (1) is not applicable to the case where the output feature map of the convolutional layer shown in Figure4 is halved with respect to the size of the identity map feature map and the number of channels is doubled. Therefore, the dashed connection of the identity mapping in the R3D network structure adopted in this paper adopts the method in scheme (2). By adding a convolutional layer with a convolution kernel size of 1 × 1 × 1 and a convolution stride of 1 × 2 × 2 or 2 × 2 × 2, the input feature map size and number of channels of the two branches are kept consistent.The structure of the R3D model used in this paper is shown in Figure5.Figure 5
R3D network structure diagram.The 3D convolution operation simultaneously extracts video spatial and temporal dimensions through a 3D convolution kernel. According to the three-dimensional convolution properties, spatial modeling and temporal modeling can be decomposed into two separate steps, which are replaced by two-dimensional spatial convolution (2D convolution) and one-dimensional temporal convolution (1D convolution). This process is called three-dimensional convolution decomposition, which is also called (2 + 1)D decomposition vividly.The 3D convolutional network preserves both temporal and spatial information through layer-by-layer transfer. In this paper, a video clip is fed into a 3D convolutional neural network. We assume that the tensor generated by thei-th convolutional layer is zi, then zi is a four-dimensional tensor of size Ni × L × Hi × Wi, where Ni is the number of convolution kernels of the i-th convolutional layer, L is the time dimension of the feature map, and Hi × Wi is the spatial dimension of the feature map. Each convolution kernel is a four-dimensional tensor of size Ni-1 × t × d × d, where Ni−1 is the number of convolution kernels of the i − 1-th convolutional layer, t is the size of the three-dimensional convolution time dimension, and d × d is the size of the three-dimensional convolution space dimension.The (2 + 1)D decomposition is a decomposition of a three-dimensional convolution kernel withNi tensor size Ni−1 × t × d × d into a two-dimensional spatial convolution kernel with Mi tensor size Ni−1 × 1 × d × d and a one-dimensional temporal convolution kernel with Ni tensor size Mi × t × 1 × 1. The hyperparameter Mi determines the subspace dimension of the feature map between spatial convolution and temporal convolution. In order to keep the parameters of the network before and after decomposition unchanged, the calculation formula of Mi is as follows:(26)Mi=td2Ni−1Nid2Ni−1+tNi.Figure6 shows the 3D decomposition process when the input tensor zi−1 is a single channel (that is, zi−1). If the 3D convolution has a span in space or time (implementing downsampling), it should be decomposed in the spatial and temporal dimensions accordingly.Figure 6
3D convolution decomposition process.Compared with the 3D convolution, the (2 + 1) D decomposed convolution module does not reduce the number of parameters, but a ReLU layer is added between the 2D convolution and the 1D convolution. This leads to an increase in the number of nonlinear functions in the network, allowing the network to fit more complex functions. The above-mentioned (2 + 1)D decomposition process does not change the number of parameters of the network, and the server memory requirements are relatively high when training the network.This paper improves on the above (2 + 1)D decomposition. Specifically, this paper directly decomposes Ni three-dimensional convolution kernels of size t × d × d into Ni two-dimensional spatial convolution kernels of size 1 × d × d and Ni one-dimensional temporal convolution kernels of size t × 1 × 1. The improved (2 + 1) D decomposition can greatly reduce the network parameters and speed up the network operation.The R (2 + 1)D network used in this paper is a network formed by decomposing the convolutional layer according to the improved (2 + 1)D decomposition process based on the R3D network used in the previous section. The three-dimensional convolution kernel of 3 × 3 × 3 is decomposed into a two-dimensional spatial convolution kernel of 1 × 3 × 3 and a one-dimensional temporal convolution kernel of 3 × 1 × 1. The specific decomposition process is shown in Figure7, where Conv_s represents the decomposition in the spatial domain and Conv_t represents the decomposition in the time domain.Figure 7
(2 + 1) D decomposition process.The structure diagram of the R (2 + 1) D network model is shown in Figure8.Figure 8
Structure diagram of R (2 + 1) D network.


## 4. A Two-Channel Convolutional Network for Calligraphic Style Feature Extraction and Discrimination

The third part constructs a calligraphy style feature extraction and identification system based on a two-channel convolutional network. The authenticity identification process of calligraphy works is generally divided into four steps: scanning of original works, preprocessing of original works, extraction of feature points, comparison and matching of authentic and fake works, and performance evaluation of the identification system. The general process of calligraphy identification is shown in Figure9(a). The system structure diagram is shown in Figure 9(b).Figure 9
System design diagram. (a) Calligraphy identification block diagram. (b) System structure diagram.
(a)(b)The computer-aided identification of authenticity of Chinese calligraphy is based on the overall style characteristics of calligraphers, and a database of authentic works of different calligraphers should be built during feature extraction and identification. A feature database is established by extracting feature data of the works. The algorithm first scans the original calligraphy works to obtain digitized works and then divides the pages to obtain single subimages. By extracting the outline skeleton and stroke features of calligraphy characters, the algorithm calculates the feature weight and the authenticity judgment function and constructs the authenticity identification system. Figure9(b) is the structure diagram of the authenticity identification system.Figure10 shows an example of calligraphy image recognition proposed in this paper.Figure 10
Example of calligraphy image recognition. (a) Original calligraphy image. (b) Feature extraction image.
(a)(b)On the basis of the above research, the effect of the calligraphy style feature extraction and identification system based on the dual-channel convolutional neural network proposed in this study is verified, and the results are shown in Table1.Table 1
Performance verification of the calligraphy style feature extraction and identification system based on the two-channel convolutional neural network.
NumberFeature extractionCalligraphy identificationNumberFeature extractionCalligraphy identification195.4088.442392.2392.90293.3191.182492.6190.85392.4291.002591.3686.01493.7490.422695.7889.15591.2691.142795.5192.90691.8892.322892.0492.23795.8491.392990.5787.78894.4987.943090.9592.01990.5088.173190.3789.011095.3392.673291.6788.791190.1187.353390.7291.061293.0787.783492.1188.221395.0391.103593.3390.001493.7792.763690.3586.111594.7188.933794.1788.531691.5390.413895.3988.181790.8289.423993.1290.721892.0990.694095.7192.431994.1191.134193.3587.092094.5891.994290.6788.962192.4586.254393.5290.412295.2588.494493.7392.64From the above research, it can be seen that the calligraphy style feature extraction and identification system based on the dual-channel convolutional neural network proposed in this paper has a good performance in calligraphy style feature extraction and identification.


## 5. Conclusion

The contour of calligraphy is also called edge detection, and the result of extraction is several closed contour curves formed by contour tracking. Contour feature extraction is a common method in image processing. When extracting the features of calligraphy characters, the contour edge features of calligraphy characters are generally extracted first, and the extracted contour features can eliminate a large amount of redundant information, which is beneficial to the acquisition of subsequent feature points. Moreover, the refinement of calligraphic characters is very important in calligraphic character feature extraction and style learning. This paper proposes a calligraphy style feature extraction and identification technology based on a two-channel convolutional neural network, constructs an intelligent calligraphy-style feature extraction and identification system, and improves the effect of calligraphy style feature extraction and identification. The research shows that the calligraphy style feature extraction and identification system based on the two-channel convolutional neural network proposed in this paper has a good performance in calligraphy style feature extraction and identification.



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