The Recognition of Handwritten Digital Based on BP Neural Network

2013 ◽  
Vol 416-417 ◽  
pp. 1239-1243
Author(s):  
Shan Gao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Practical Significance ◽  
Test Results ◽  
Recognition Method ◽  
Number Recognition ◽  
Recognition Ability ◽  
Fuzzy Recognition ◽  
Zip Code

The article put forward to new recognition method of handwritten digital based on BP neural network. Its recognition process mainly includes ten aspect: incline correction of handwritten number, edge detection and separation of a set number, binarization, denoising, extraction of numerals, window scaling, location standardization, thinning, extraction of numeral feature and fuzzy recognition based on BP neural network. The test results show that the recognition rate of this method can be over 92 percent. The recognition time of characters for character is less than 1.1 second, which means that the method is more effective recognition ability and can better satisfy the real system requirements.It should be widely applied practical significance for Book Number Recognition, zip code recognition sorting.

Machine-Printed Invoice Number Based on Fuzzy Recognition

2012 ◽  
Vol 214 ◽  
pp. 705-710 ◽  
Author(s):  
Xiao Ping Xian
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Real System ◽  
Recognition Method ◽  
Recognition Time ◽  
Recognition Ability ◽  
System Requirements ◽  
Fuzzy Recognition ◽  
Detection And Separation

A new fuzzy recognition method of machine-printed invoice number based on neural network is presented. This method includes ten links: invoice number detection and separation of right on top of invoice, binarization, denoising, incline correction, extraction of invoice code numerals, window scaling, location standardization, thinning, extraction of numeral feature and fuzzy recognition based on BP neural network. Through testing, the recognition rate of this method can be over 99%.The recognition time of characters for character is less than 1 second, which means that the method is of more effective recognition ability and can better satisfy the real system requirements.

Free Handwritten Numeral Recognition Based on BP Neural Network

2013 ◽  
Vol 850-851 ◽  
pp. 909-912
Author(s):  
Miao Chao Chen ◽  
Fang Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Matlab Simulation ◽  
Recognition Method ◽  
Simulation Experiments ◽  
Handwritten Numeral Recognition ◽  
Numeral Recognition ◽  
Important Branch ◽  
Application Prospects

Handwritten numeral recognition is an important branch in the field of pattern recognition, has broad application prospects. This article presents a method of using BP Neural Network to implement programme for recognition of free handwritten numerals. Scanned handwritten numeral image after preprocessing and feature extraction, classificated and recognized by the BP Neural Network. Through Matlab simulation experiments it shows that the recognition method is effective and has high recognition rate.

scholarly journals CAPTCHA Recognition Method Based on CNN with Focal Loss

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhong Wang ◽  
Peibei Shi
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Loss Function ◽  
Neural Network Model ◽  
Recognition Rate ◽  
Recognition Method ◽  
High Adhesion ◽  
Recognition Ability ◽  
Different Types ◽  
Simple Neural Network

In order to distinguish between computers and humans, CAPTCHA is widely used in links such as website login and registration. The traditional CAPTCHA recognition method has poor recognition ability and robustness to different types of verification codes. For this reason, the paper proposes a CAPTCHA recognition method based on convolutional neural network with focal loss function. This method improves the traditional VGG network structure and introduces the focal loss function to generate a new CAPTCHA recognition model. First, we perform preprocessing such as grayscale, binarization, denoising, segmentation, and annotation and then use the Keras library to build a simple neural network model. In addition, we build a terminal end-to-end neural network model for recognition for complex CAPTCHA with high adhesion and more interference pixel. By testing the CNKI CAPTCHA, Zhengfang CAPTCHA, and randomly generated CAPTCHA, the experimental results show that the proposed method has a better recognition effect and robustness for three different datasets, and it has certain advantages compared with traditional deep learning methods. The recognition rate is 99%, 98.5%, and 97.84%, respectively.

A New Way for Handwritten Numeral Recognition

2013 ◽  
Vol 798-799 ◽  
pp. 643-646
Author(s):  
Bao Lin Guan ◽  
Li Deng Ba
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Bp Algorithm ◽  
Recognition Method ◽  
Handwritten Numeral Recognition ◽  
Recognition Experiment ◽  
Numeral Recognition ◽  
Crossover And Mutation ◽  
And Training

Handwritten numeral recognition method generally uses neural networks, the more prominent of these is BP neural network, but BP algorithm is easily get in a local minimum of the error-prone and causes slow oscillation and training , general solution for it is to optimize the structure of the algorithms first. Therefore, on the basis of the analysis of GA-BP algorithm, propose a method of making the appropriate operators of GA such as crossover and mutation probability, optimizing the weights and thresholds of BP Neural Network with the improved GA. At handwritten numeral recognition experiment, the results show that the method has faster convergence and more reliable stability, greatly improved BP neural network for learning and recognition rate.

Partial Discharge Pattern Recognition Method for GIS Based on GA-BPNN

2015 ◽  
Vol 738-739 ◽  
pp. 397-400
Author(s):  
Xin Yan Feng ◽  
Xiao Li Hu ◽  
Jun Yong ◽  
Bo Yang ◽  
Xiao Bin Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Partial Discharge ◽  
Recognition Rate ◽  
Correct Identification ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Different Types ◽  
Signal Peak ◽  
Adaptive Momentum

In order to study different types of partial discharge inspired by defects in GIS and increase the rate of correct identification on defects, four kinds of typical insulation defects physical model are designed based on the insulation defects of 110 kV GIS and its partial discharge characteristics. Ten feature parameters including the signal peak and kurtosis are acquired from 222 groups of partial discharge signal data, and recognized by BP neural network which is optimized by input genetic algorithm. Recognition results show that this method works well, owning a higher recognition rate than adaptive momentum BP neural network

scholarly journals Consumer Decision-Making Power Based on BP Neural Network and Fuzzy Mathematical Model

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Weijie Li
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Decision Making ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Real Life ◽  
Practical Significance ◽  
Fuzzy Mathematics ◽  
Artificial Neural

In real life, because of the uncertainty of risk, incomplete information, perceived cost, and other factors, there are irrational behaviors in the decision-making power of consumers, so it is of great practical significance to study the decision-making power of consumers in the choice of countermeasures and personalized product recommendation. The purpose of this paper is to analyze the decision-making power of consumers based on the BP neural network and fuzzy mathematical model. First, the basic theory of artificial neural network and the concepts of set theory and fuzzy reasoning of fuzzy mathematics are described. Second, the behavior prediction model with the equal emphasis on rationality and irrationality and the integration of artificial neural network and fuzzy mathematics are constructed. The comments of a certain mobile phone are selected as the experimental objects to analyze the decision-making reasoning and prediction of individual consumers in the network and the decision-making reasoning of group consumers in the network, the experimental results show that through Mamdani reasoning, behavioral   intention = 5.72 . Through the fuzzy set processing, it is finally determined that the consumer’s purchase intention is close to the VT mode, which is “very inclined.” In the first method, the user’s recognition rate of product C1 is 82%, and in the second method, the user’s recognition rate is 55%. The comparison of the two methods is in line with the expectation. The first method extracts the user’s emotion and evaluation information from the comments, fully considers the personalized needs of consumers, and is closer to the prediction results of the system.

Study for Workpiece Identity Based on the BP Neural Network and the Zernike Moment

2013 ◽  
Vol 694-697 ◽  
pp. 1958-1963 ◽  
Author(s):  
Xian Wei ◽  
Jing Dong Zhang ◽  
Xue Mei Qi
Keyword(s):  
Neural Network ◽  
Fault Tolerance ◽  
Parallel Processing ◽  
Bp Neural Network ◽  
Complex Shape ◽  
Learning Ability ◽  
Zernike Moment ◽  
Recognition Ability ◽  
Identity Based ◽  
Self Learning

The robots identify, locate and install the workpiece in FMS system by identifying the characteristic information of target workpiece. The paper studied the recognition technology of complex shape workpiece with combination of BP neural network and Zernike moment. The strong recognition ability of Zernike moment can extract the characteristic. The good fault tolerance, classification, parallel processing and self-learning ability of BP neural network can greatly improve the accurate rate of recognition. Experimental results show the effectiveness of the proposed method.

scholarly journals The Recognition Method of MQAM Signals Based on BP Neural Network and Bird Swarm Algorithm

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 36078-36086
Author(s):  
Chengchang Zhang ◽  
Sa Yu ◽  
Guojun Li ◽  
Yu Xu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Method ◽  
Swarm Algorithm ◽  
Bird Swarm Algorithm

scholarly journals Research on recognition method of cloud precipitation particle shape based on BP neural network

2021 ◽  
Vol 336 ◽  
pp. 06011
Author(s):  
Haonan Dong ◽  
Ruili Jiao ◽  
Minsong Huang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Particle Shape ◽  
Northern China ◽  
Recognition Method ◽  
Cloud Particle ◽  
Particle Images ◽  
Bp Neural Network Model

In order to solve the problem that the shape of cloud particle images measured by airborne cloud imaging probe (CIP) cannot be automatically recognized, this paper proposes an automatic recognition method of cloud and precipitation particle shape based on BP neural network. This method mainly uses a set of geometric parameters which can better describe the shape characteristics of cloud precipitation particles. Based on the cloud precipitation particle images measured by CIP in the precipitation stratiform clouds in northern China, a particle shape data training set and a testing set were constructed to train and verify the effect of the selected BP neural network model. The selected BP neural network model can classify the cloud particle image into tiny, column, needle, dendrite, aggregate, graupel, sphere, hexagonal and irregular. Utilizing the field campaign data measured by CIP, the habit identified results by the improved Holroyd method and by the selected BP neural network model were compared, which shows that the accuracy of BP neural network method is better than that of improved Holroyd method.

The Application of BP Neural Network in Network Intrusion Detection

2013 ◽  
Vol 765-767 ◽  
pp. 1415-1418 ◽  
Author(s):  
Ya Fang Lou ◽  
Zhi Jun Yuan ◽  
Hao Wu
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Bp Neural Network ◽  
Practical Significance ◽  
Network Intrusion Detection ◽  
Critical Problem ◽  
Detection Model ◽  
Detection Systems ◽  
Network Intrusion ◽  
Detection Technology

As the network is impacting enormously to all aspects of society, the network security becomes a critical problem. The traditional intrusion detection technology exists some disadvantages: the imperfection of architecture, the slow detecting of system, the vulnerable of itself architecture, and so on. This paper presents an intrusion detection model based on BP neural network which has the incomparable advantages against traditional intrusion detection systems. Therefore, the study of this subject possesses the practical significance.

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