Research on Unconstrained Handwritten Numeral Recognition by BP Feature Screening Based on Fuzzy Clustering

2015 ◽  
Vol 734 ◽  
pp. 504-507
Author(s):  
Pei Ye ◽  
Tao Jiang
Keyword(s):  
Fuzzy Clustering ◽  
Recognition Rate ◽  
Recognition System ◽  
Bp Network ◽  
Feature Screening ◽  
Handwritten Numeral Recognition ◽  
Traditional Algorithm ◽  
Fuzzy Relation Matrix ◽  
Numeral Recognition ◽  
Very High

In this paper, the recognition system of fuzzy clustering based on BP feature screening was put out. The figure specimens of experiment were filtered through BP network, and the result of screening was fit into the clustering source. At last fuzzy clustering was carried out by constituting the fuzzy relation matrix. The result of experiment demonstrates that this method has very high noise immunity capacity and overcame the limitation of traditional algorithm with single factor recognition. The recognition rate and precision ratio were greatly improved at the same time.

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.

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

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.

Multi-Bank Check Recognition System: Consideration on the Numeral Amount Recognition Module

1997 ◽  
Vol 11 (04) ◽  
pp. 595-618 ◽  
Author(s):  
L. Heutte ◽  
P. Barbosa-Pereira ◽  
O. Bougeois ◽  
J. V. Moreau ◽  
B. Plessis ◽  
...  
Keyword(s):  
Contextual Information ◽  
Recognition Rate ◽  
Recognition System ◽  
Structural Features ◽  
Contextual Analysis ◽  
Syntactic Analysis ◽  
Good Representation ◽  
Automatic Reading ◽  
Reading System ◽  
Numeral Recognition

This paper presents a complete numeral amount recognition module which is integrated in an automatic system aimed at reading all types of French checks. This module is combined with an automatic reading system of literal amounts. This complete working system, called LIREChèques, is developed by MATRA MS&I and is now in advanced test at SERINTEL, a pilot site. Two aspects of the numeral amount recognition system are particularly emphasized: the numeral recognition stage itself and the syntactic analysis stage. The numeral recognition module relies on a combination of two individual classifiers, the first one is based on concavity measurements, the second one on both statistical and structural features. The syntactic analysis, called syntactic/contextual analysis, is combined with contextual information to take into account the segmentation behaviour and the presence of literal entities in the numeral amount. We demonstrate that very good performances can be obtained on digits such as those extracted from numeral amounts since a substitution rate of 0.06% while still preserving a recognition rate of near 87% can be achieved. As for the syntactic/contextual analysis stage, results obtained on a test set (containing checks from more than 40 different banks and 15/ of typed checks, thus being a good representation of the real tests realized on site) show clearly that introduction of contextual information in association with syntactic analysis allows to process much more numeral amounts than a simple syntactic analysis and increases perceptibility of the 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.

Study of Face Orientation Recognition Based on Neural Network

2018 ◽  
Vol 32 (11) ◽  
pp. 1856015 ◽  
Author(s):  
Suping Li ◽  
Zhanfeng Wang ◽  
Jing Wang
Keyword(s):  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Training Data ◽  
Future Research ◽  
Identification System ◽  
Bp Network ◽  
Feature Vectors ◽  
And Performance ◽  
The Stability

Learning vector quantization (LVQ) network and back-propagation (BP) network are constructed easily making use of MATLAB toolbox on the basis of maintaining the recognition rate. Face images are randomly selected from images set as training data of LVQ network and BP network. LVQ algorithm and BP algorithm are used to train network. The automatic recognition of face orientation is realized when the system obtains convergence network. First, all images are processed by edge detection. Then feature vectors representing position of the eye were extracted from edge detected images. Feature vectors of training set are sent to network to adjust the parameters which ensures the convergence speed and performance of the network. Experimental results show that the constructed LVQ network and BP network can judge face orientation according to feature vectors of input images. Generally, the recognition rate of LVQ network is higher than that of BP network. The LVQ network and BP network are both feasible and effective for face orientation recognition to some extent. The advantage of this work is that the recognition system is efficient and easy to promote. This paper focuses on how to use MATLAB easily to design identification network rather than the complexity of identification system. The future research will focus on the stability and robustness of recognition network.

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