Judgment of Slang Based on Character Feature and Feature Expression Based on Slang’s Context Feature

2016 ◽  
pp. 280-288
Author(s):  
Kazuyuki Matsumoto ◽  
Seiji Tsuchiya ◽  
Minoru Yoshida ◽  
Kenji Kita
Keyword(s):  
Character Feature ◽  
Feature Expression

Handwritten Balinesse Character Recognition using K-Nearest Neighbor

2018 ◽  
Author(s):  
I Wayan Agus Surya Darma
Keyword(s):  
Feature Extraction ◽  
Success Rate ◽  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Extraction Process ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Character Feature

Balinese character recognition is a technique to recognize feature or pattern of Balinese character. Feature of Balinese character is generated through feature extraction process. This research using handwritten Balinese character. Feature extraction is a process to obtain the feature of character. In this research, feature extraction process generated semantic and direction feature of handwritten Balinese character. Recognition is using K-Nearest Neighbor algorithm to recognize 81 handwritten Balinese character. The feature of Balinese character images tester are compared with reference features. Result of the recognition system with K=3 and reference=10 is achieved a success rate of 97,53%.

A Feature Extracting Method for Chinese Character Recognition of License Plate Based on Elastic Mesh and Wavelet Transform

2013 ◽  
Vol 760-762 ◽  
pp. 1452-1456
Author(s):  
Chao Zheng ◽  
Hua Yang ◽  
Xing Yang ◽  
Chao Chao Huang ◽  
Xiao Di Wu
Keyword(s):  
Wavelet Transform ◽  
Character Recognition ◽  
Chinese Character ◽  
Wavelet Coefficient ◽  
Difficult Problem ◽  
License Plate ◽  
Structure Information ◽  
Feature Extracting ◽  
Chinese Character Recognition ◽  
Character Feature

Low-resolution Chinese character recognition of license plate is always a difficult problem. For solving it, we must think about the distinctiveness of character feature and the counting speed of method simultaneously. In this paper, we proposed a simple and effective feature extraction algorithm. First, extract the statistical feature of Chinese character based on decomposing stroke with wavelet transform. Second, apply Elastic Mesh Algorithm into extracting wavelet coefficient of decomposing stroke to get the structure information of Chinese character. The experimental results show the method is robust against low quality Chinese characters, such as skew, fuzzy, glue, distorted character, and easy to be used in actual projects with simple advantage.

scholarly journals Least-squares method and deep learning in the identification and analysis of name-plates of power equipment

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yerong Zhong ◽  
Guoheng Ruan ◽  
Ehab Abozinadah ◽  
Jiaming Jiang
Keyword(s):  
Deep Learning ◽  
Least Squares ◽  
Least Squares Method ◽  
Feature Fusion ◽  
Learning Algorithm ◽  
Power Equipment ◽  
Practical Application ◽  
Deep Learning Algorithm ◽  
Character Feature ◽  
Histogram Feature

Abstract This article proposes a nameplate recognition method based on the least-squares method and deep learning algorithm character feature fusion. This method extracts the histogram of the edge direction of the character and constructs the histogram feature vector based on the wavelet transform deep learning algorithm. We use classifier training for the text recognition of the nameplate to segment the text into individual characters. Then, we extract the character features to build a template. Experiments prove that the algorithm meets the practical application needs of nameplate identification of power equipment and achieves the design goals.

scholarly journals Fine-Grained Pests Recognition Based on Truncated Probability Fusion Network via Internet of Things in Forestry and Agricultural Scenes

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 290
Author(s):  
Kai Ma ◽  
Ming-Jun Nie ◽  
Sen Lin ◽  
Jianlei Kong ◽  
Cheng-Cai Yang ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Insect Pests ◽  
Accurate Identification ◽  
Technical Application ◽  
Network Mining ◽  
Fine Grained ◽  
Feature Expression ◽  
The Internet Of Things ◽  
Different Levels ◽  
Better Than

Accurate identification of insect pests is the key to improve crop yield and ensure quality and safety. However, under the influence of environmental conditions, the same kind of pests show obvious differences in intraclass representation, while the different kinds of pests show slight similarities. The traditional methods have been difficult to deal with fine-grained identification of pests, and their practical deployment is low. In order to solve this problem, this paper uses a variety of equipment terminals in the agricultural Internet of Things to obtain a large number of pest images and proposes a fine-grained identification model of pests based on probability fusion network FPNT. This model designs a fine-grained feature extractor based on an optimized CSPNet backbone network, mining different levels of local feature expression that can distinguish subtle differences. After the integration of the NetVLAD aggregation layer, the gated probability fusion layer gives full play to the advantages of information complementarity and confidence coupling of multi-model fusion. The comparison test shows that the PFNT model has an average recognition accuracy of 93.18% for all kinds of pests, and its performance is better than other deep-learning methods, with the average processing time drop to 61 ms, which can meet the needs of fine-grained image recognition of pests in the Internet of Things in agricultural and forestry practice, and provide technical application reference for intelligent early warning and prevention of pests.

scholarly journals Learning to Write Stylized Chinese Characters by Reading a Handful of Examples

2018 ◽  
Author(s):  
Danyang Sun ◽  
Tongzheng Ren ◽  
Chongxuan Li ◽  
Hang Su ◽  
Jun Zhu
Keyword(s):  
Chinese Character ◽  
Optimization Method ◽  
Chinese Characters ◽  
Training Set ◽  
Generalization Ability ◽  
Complex Shapes ◽  
Character Feature

Automatically writing stylized characters is an attractive yet challenging task, especially for Chinese characters with complex shapes and structures. Most current methods are restricted to generate stylized characters already present in the training set, but required to retrain the model when generating characters of new styles. In this paper, we develop a novel framework of Style-Aware Variational Auto-Encoder (SA-VAE), which disentangles the content-relevant and style-relevant components of a Chinese character feature with a novel intercross pair-wise optimization method. In this case, our method can generate Chinese characters flexibly by reading a few examples. Experiments demonstrate that our method has a powerful one-shot/few-shot generalization ability by inferring the style representation, which is the first attempt to learn to write new-style Chinese characters by observing only one or a few examples. 

scholarly journals Person Reidentification Model Based on Multiattention Modules and Multiscale Residuals

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yongyi Li ◽  
Shiqi Wang ◽  
Shuang Dong ◽  
Xueling Lv ◽  
Changzhi Lv ◽  
...  
Keyword(s):  
Local Features ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Original Network ◽  
Fine Grained ◽  
Backbone Network ◽  
Model Based ◽  
Local Branch ◽  
Feature Expression ◽  
Global And Local

At present, person reidentification based on attention mechanism has attracted many scholars’ interests. Although attention module can improve the representation ability and reidentification accuracy of Re-ID model to a certain extent, it depends on the coupling of attention module and original network. In this paper, a person reidentification model that combines multiple attentions and multiscale residuals is proposed. The model introduces combined attention fusion module and multiscale residual fusion module in the backbone network ResNet 50 to enhance the feature flow between residual blocks and better fuse multiscale features. Furthermore, a global branch and a local branch are designed and applied to enhance the channel aggregation and position perception ability of the network by utilizing the dual ensemble attention module, as along as the fine-grained feature expression is obtained by using multiproportion block and reorganization. Thus, the global and local features are enhanced. The experimental results on Market-1501 dataset and DukeMTMC-reID dataset show that the indexes of the presented model, especially Rank-1 accuracy, reach 96.20% and 89.59%, respectively, which can be considered as a progress in Re-ID.

scholarly journals A New Multi-Scale Convolutional Model Based on Multiple Attention for Image Classification

2019 ◽  
Vol 10 (1) ◽  
pp. 101 ◽  
Author(s):  
Yadong Yang ◽  
Chengji Xu ◽  
Feng Dong ◽  
Xiaofeng Wang
Keyword(s):  
Image Classification ◽  
Attention Mechanism ◽  
Natural Scenes ◽  
Significant Information ◽  
Convolutional Networks ◽  
Multi Scale ◽  
Model Based ◽  
Convolution Model ◽  
Grouping Method ◽  
Feature Expression

Computer vision systems are insensitive to the scale of objects in natural scenes, so it is important to study the multi-scale representation of features. Res2Net implements hierarchical multi-scale convolution in residual blocks, but its random grouping method affects the robustness and intuitive interpretability of the network. We propose a new multi-scale convolution model based on multiple attention. It introduces the attention mechanism into the structure of a Res2-block to better guide feature expression. First, we adopt channel attention to score channels and sort them in descending order of the feature’s importance (Channels-Sort). The sorted residual blocks are grouped and intra-block hierarchically convolved to form a single attention and multi-scale block (AMS-block). Then, we implement channel attention on the residual small blocks to constitute a dual attention and multi-scale block (DAMS-block). Introducing spatial attention before sorting the channels to form multi-attention multi-scale blocks(MAMS-block). A MAMS-convolutional neural network (CNN) is a series of multiple MAMS-blocks. It enables significant information to be expressed at more levels, and can also be easily grafted into different convolutional structures. Limited by hardware conditions, we only prove the validity of the proposed ideas through convolutional networks of the same magnitude. The experimental results show that the convolution model with an attention mechanism and multi-scale features is superior in image classification.

scholarly journals Prediction of protein structural classes by different feature expressions based on 2-D wavelet denoising and fusion

2019 ◽  
Vol 20 (S25) ◽  
Author(s):  
Shunfang Wang ◽  
Xiaoheng Wang
Keyword(s):  
Recognition Rate ◽  
Wavelet Denoising ◽  
Vital Role ◽  
Rational Drug Design ◽  
Redundant Information ◽  
Structural Class ◽  
Protein Structural Class ◽  
Feature Vectors ◽  
Feature Expression ◽  
Structural Classes

Abstract Background Protein structural class predicting is a heavily researched subject in bioinformatics that plays a vital role in protein functional analysis, protein folding recognition, rational drug design and other related fields. However, when traditional feature expression methods are adopted, the features usually contain considerable redundant information, which leads to a very low recognition rate of protein structural classes. Results We constructed a prediction model based on wavelet denoising using different feature expression methods. A new fusion idea, first fuse and then denoise, is proposed in this article. Two types of pseudo amino acid compositions are utilized to distill feature vectors. Then, a two-dimensional (2-D) wavelet denoising algorithm is used to remove the redundant information from two extracted feature vectors. The two feature vectors based on parallel 2-D wavelet denoising are fused, which is known as PWD-FU-PseAAC. The related source codes are available at https://github.com/Xiaoheng-Wang12/Wang-xiaoheng/tree/master. Conclusions Experimental verification of three low-similarity datasets suggests that the proposed model achieves notably good results as regarding the prediction of protein structural classes.

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