Design and Realization of the SMT Product Character Recognition System

2010 ◽  
Vol 139-141 ◽  
pp. 1736-1739
Author(s):  
Hui Huang Zhao ◽  
De Jian Zhou ◽  
Zhao Hua Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Character Recognition ◽  
Median Filter ◽  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Network Module ◽  
Pass Filter ◽  
Low Pass Filter

We present an approach to recognizing characters in surface mount technology (SMT) product. An improved SMT product character recognition method is proposed which can obtain a good recognition rate. Some appropriate image processing algorithms, such as Gray processing, Low-pass Filter, Median Filter, and so on, are used to eliminate the noise. Then, Character image is obtained after character segmentation and character normalization. Finally, a three-layer back propagation (BP) neural network module is constructed. In order to improve the convergence rate of the network and avoid oscillation and divergence, the BP algorithm with momentum item is used. As a result, the SMT product character recognition system is developed. Experimental results indicate that the proposed character recognition can obtain satisfactory character-recognition rate and the recognition rate reached over by 98.6% when the hidden layer of BP neural network module has 20 nodes.

A STUDY OF DIGITAL WATERMARKING RECOGNITION USING ORTHOGONAL CODE SEQUENCES WITH A BACK-PROPAGATION NEURAL NETWORK

2013 ◽  
Vol 37 (3) ◽  
pp. 459-465
Author(s):  
Chih-Ta Yen ◽  
Ing-Jr Ding ◽  
Zong-Wei Lai
Keyword(s):  
Neural Network ◽  
Digital Watermarking ◽  
Median Filter ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Noise Interference ◽  
Low Pass ◽  
Neural Network Algorithm

Digital watermarking is an encryption technology commonly used to protect intellectual property and copyright. In this study, we restored watermarks that had already been affected by noise interference, used the Walsh–Hadamard codes as the watermark identification codes, and applied salt-and-pepper noise and Gaussian noise to destroy watermarks. First method, we used a low-pass filter and median filter to remove noise interferences. The second one, we used a back-propagation neural network algorithm to suppress noises. We removed nearly all noise and recovered the originally embedded watermarks of Walsh–Hadmard codes.

A Coded Digital Watermarking Recognition Using Neural Network Method

2013 ◽  
Vol 284-287 ◽  
pp. 2961-2964
Author(s):  
Chih Ta Yen ◽  
Ing Jr Ding ◽  
Zong Wei Lai
Keyword(s):  
Neural Network ◽  
Digital Watermarking ◽  
Median Filter ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Noise Interference ◽  
Low Pass ◽  
Neural Network Algorithm

Digital watermarking is an encryption technology commonly used to protect intellectual property and copyright. Although watermarks possess advantageous secrecy and robustness, environmental interference in the image propagation through the Internet is inevitable and, certainly, human-based image modification can also destroy the watermark. In this study, we restored watermarks that had already been affected by noise interference, used the Walsh-Hadamard codes as the watermark identification codes, and applied salt-and-pepper noise and Gaussian noise to destroy watermarks. First, we used a low-pass filter and median filter to remove noise interferences. Although these filters can suppress noises, watermarked images remain unidentifiable when the noise interferences strongly. Finally, we used a back-propagation neural network algorithm to filter noises, obtaining results that exceeded our expectations. We removed nearly all noise and recovered the originally embedded watermarks of Walsh-Hadmard codes.

scholarly journals UNSTABLE MORSE CODE RECOGNITION SYSTEM WITH EXPERT-GATING NEURAL NETWORK

2002 ◽  
Vol 14 (01) ◽  
pp. 12-19 ◽  
Author(s):  
DUU-TONG FUH ◽  
CHING-HSING LUO
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Disabled Persons ◽  
Single Chip ◽  
Morse Code ◽  
Average Recognition Rate ◽  
Rate Test

The standard Morse code defines the tone ratio (dash/dot) and the silent ratio (dash-space/dotspace) as 3:1. Since human typing ratio can't keep this ratio precisely and the two ratios —tone ratio and silent ratio—are not equal, the Morse code can't be recognized automatically. The requirement of the standard ratio is difficult to satisfy even for an ordinary person. As for the unstable Morse code typing pattern, the auto-recognition algorithms in the literature are not good enough in applications. The disabled persons usually have difficulty in maintaining a stable typing speeds and typing ratios, we therefore adopted an Expert-Gating neural network model to implement in single chip and recognize online unstable Morse codes. Also, we used another method—a linear back propagation recalling algorithm, to implement in single chip and recognize unstable Morse codes. From three person tests: Test one is a cerebral palsy; Test two is a beginner: Test three is a skilled expert, we have the results: in the experiment of test one, we have 91.15% (use 6 characters average time series as thresholds) and 91.54% (learning 26 characters) online average recognition rate; test two have 95.77% and 96.15%, and test three have 98.46% and 99.23% respectively. As for linear back propagation recalling method online recognized rate, we have the results from test one: 92.31% online average recognition rate; test two: 96.15%; and test three 99.23% respectively. So, we concluded: The Expert-Gating neural network and the linear back propagation recalling algorithm have successfully overcome the difficulty of analyzing a severely online unstable Morse code time series and successfully implement in single chip to recognize online unstable Morse code.

Study of Digital Character Recognition Based on BP Neural Networks

2013 ◽  
Vol 333-335 ◽  
pp. 856-859 ◽  
Author(s):  
Shuai Yuan ◽  
Guo Yun Zhang ◽  
Jian Hui Wu ◽  
Long Yuan Guo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Character Recognition ◽  
Back Propagation ◽  
Solution Space ◽  
Training Sample ◽  
Back Propagation Algorithm ◽  
Sample Object ◽  
Training Error ◽  
Hidden Layer

A digital character recognition method is presented based on BP Neural Network. This paper preprocesses the digital character image and extracts character feature, then uses BP Neural Network to recognize digital character. Back Propagation algorithm seeks network weights to minimize training error in the solution space. A network with hidden layer is created at first, then an input sample vector is sent to network input terminal and the square error E between output values and training sample object output values is calculated. Above process is repeated for input samples of training sets until the error is reduced within the limits of the threshold. The results show that the method presented has good accuracy, quick speed and strong robustness for realtime application.

Research and Application of BP Neural Network in Specific Power Harmonic Detection

2012 ◽  
Vol 588-589 ◽  
pp. 379-383
Author(s):  
Xiao Dan Wang ◽  
Li Xin Ma ◽  
Yue Xiao Wang ◽  
Shu Juan Yuan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Reactive Power ◽  
Supply Voltage ◽  
Harmonic Component ◽  
Specific Power ◽  
Nonlinear Loads ◽  
Solution Quality ◽  
Pass Filter ◽  
Low Pass Filter

APF (Active Power Filter) is widely used in power system harmonic control and reactive power compensation, has been proven as an effective method to overcome various power quality issues such as unbalanced source current, large reactive power harmonic and neutral currents due to the proliferation of nonlinear loads. Optimizing the performance of APF using conventional ip-iq detection method based on instantaneous reactive power theory is quite difficult because of the complex coordinate conversion, what’s more, the presence of low-pass filter will cause a certain delay. This paper proposes the implementation of BP neural network to extract specific harmonic, it can optimize the APF performance for load compensation under distorted supply voltage condition and sudden load fluctuation. Weight adjustment using the BFGS quasi-Newton algorithm, which can accurately detect the fundamental and harmonic component of the phase amplitude . Matlab simulation results demonstrate that the performance of BP neural network algorithm is superior compared to conventional method, in terms of both convergence rate and solution quality.

scholarly journals Investigation on Intelligent Recognition System of Instrument Based on Multi-step Convolution Neural Network

2020 ◽  
Vol 9 (4) ◽  
pp. 185-192
Author(s):  
Feng Shan ◽  
◽  
Hui Sun ◽  
Xiaoyun Tang ◽  
Weiwei Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Recognition Rate ◽  
Test Site ◽  
Recognition System ◽  
Image Features ◽  
Convolution Neural Network ◽  
Digital Display ◽  
Industrial Control ◽  
Intelligent Recognition

Digital instruments are widely used in industrial control, traffic, equipment displays and other fields because of the intuitive characteristic of their test data. Aiming at the character recognition scene of digital display Vernier caliper, this paper creatively proposes an intelligent instrument recognition system based on multi-step convolution neural network (CNN). Firstly, the image smples are collected from the Vernier caliper test site, and their resolution and size are normalized. Then the CNN model was established to train the image smples and extract the features. The digital display region in the image smples were extracted according to the image features, and the numbers in the Vernier caliper were cut out. Finally, using the MINIST datas set of Vernier caliper is established, and the CNN model is used to recognize it. The test results show that the overall recognition rate of the proposed CNN model is more than 95%, and has good robustness and generalization ability.

A Sketch Recognition Algorithm Based on Bayesian Network and Convolution Neural Network

2019 ◽  
Vol 23 (2) ◽  
pp. 261-267 ◽  
Author(s):  
Xiang Hou ◽  
Keyword(s):  
Neural Network ◽  
Bayesian Network ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Convolution Neural Network ◽  
Sketch Recognition ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Recognition Result ◽  
Low Pass

Most of the existing sketch recognition algorithms are used to restrict the user’s drawing habits to achieve the stroke grouping and recognition. In order to solve the problem, a new sketch recognition algorithm based on Bayesian network and convolution neural network (CNN) is proposed. First, the input sketch is processed by Gaussian low-pass filter and a smoother stroke can be obtained. The stroke of continuous input is divided, then the Bayesian network and CNN are performed on stroke recognition respectively. The recognition result of Bayesian network is adopted when the reliability of stroke is larger than the threshold, otherwise recognition result of CNN will be adopted. The experiment result shows that the proposed algorithm is effective in circuit symbol recognition. The recognition rate was achieved 80.34% in the drawing process, and the final recognition rate was achieved 93.48%.

A BACK PROPAGATION BASED REAL-TIME LICENSE PLATE RECOGNITION SYSTEM

2008 ◽  
Vol 22 (02) ◽  
pp. 233-251 ◽  
Author(s):  
YO-PING HUANG ◽  
TSUN-WEI CHANG ◽  
YEN-REN CHEN ◽  
FRODE EIKA SANDNES
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Back Propagation Neural Network ◽  
License Plate ◽  
License Plate Recognition ◽  
Video Frames ◽  
Parking Lot

License plate recognition systems have been used extensively for many applications including parking lot management, tollgate monitoring, and for the investigation of stolen vehicles. Most researches focus on static systems, which require a clear and level image to be taken of the license plate. However, the acquisition of images that can be successfully analyzed relies on both the location and movement of the target vehicle and the clarity of the environment. Moreover, only few studies have addressed the problems associated with instant car image processing. In view of these problems, a real-time license plate recognition system is proposed that recognizes the video frames taken from existing surveillance cameras. The proposed system finds the location of the license plate using projection analysis, and the characters are identified using a back propagation neural network. The strategy achieves a recognition rate of 85.8% and almost 100% after the neural network has been retrained using the erroneously recognized characters, respectively.

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