scholarly journals Design of Handwritten Numeral Recognition System Based on BP Neural Network

2021 ◽  
Vol 2025 (1) ◽  
pp. 012016
Author(s):  
Jianghai Liu ◽  
Jie Hong
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition System ◽  
Handwritten Numeral Recognition ◽  
Numeral Recognition

Design of Online Handwritten Numeral Recognition System

2012 ◽  
Vol 201-202 ◽  
pp. 329-332
Author(s):  
Yue Fen Chen ◽  
Jun Huan Lin ◽  
Guo Ping Li
Keyword(s):  
Neural Network ◽  
Memory Function ◽  
Recognition Rate ◽  
Binary Sequence ◽  
Hopfield Neural Network ◽  
Recognition System ◽  
Handwritten Numeral Recognition ◽  
Fast Recognition ◽  
Starting Point ◽  
Numeral Recognition

An effective online handwritten numeral recognition system is designed based on the Matlab GUI interface. The coordinate locations of the handwritten numerals are recorded, from which the stroke direction variations and the 2-dimensional distance between the starting point and ending point of the numeral are obtained as the features, which are encoded into 42 bits binary sequence, and then input to the Hopfield neural network. The associative memory function of the Hopfield neural network can implement the learning and recognition of the handwritten numeral. Testing results show that the designed system has high recognition rate and fast recognition speed.

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.

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.

scholarly journals A Fuzzy Logic based Handwritten Numeral Recognition System

2013 ◽  
Vol 83 (10) ◽  
pp. 36-43
Author(s):  
Mahmood KJasim ◽  
Anwar M Al-Saleh ◽  
Alaa Aljanaby
Keyword(s):  
Fuzzy Logic ◽  
Recognition System ◽  
Handwritten Numeral Recognition ◽  
Numeral Recognition

scholarly journals Classification and Application of Roof Stability of Bolt Supporting Coal Roadway Based on BP Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Heng Ren ◽  
Yongjian Zhu ◽  
Ping Wang ◽  
Peng Li ◽  
Yuqun Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Site Investigation ◽  
Recognition System ◽  
Quantitative Prediction ◽  
Widespread Application ◽  
Coal Roadway ◽  
Roof Stability ◽  
Stability Classification ◽  
The Stability

In view of the frequent occurrence of roof accidents in coal roadways supported by bolts, the widespread application of bolt support technology in coal roadways has been restricted. Through on-site investigation, numerical analysis, and other research methods, 6 evaluation indicators were determined, and according to the relevant evaluation factors and four types of coal roadway roof stability, a neural network structure for roof stability prediction was constructed to realize the quantitative prediction of the roof stability of bolt-supported coal roadway. The method of adding momentum is used to improve the BP neural network algorithm. After passing the simulation test, it is applied to the field experiment of the roof stability classification. In order to facilitate on-site application, on the basis of the established BP neural network prediction model, a coal mine roof stability classification software recognition system was developed. Using the developed software system, the stability of coal roadway roof is classified into mine, coal seam, and region. According to the recognition result, the surfer software is used to draw the contour map of the stability of the roof of each coal mining roadway. The classification results are consistent with the actual situation on site.

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