Oriya handwritten numeral recognition system

2005 ◽  
Author(s):  
K. Roy ◽  
T. Pal ◽  
U. Pal ◽  
F. Kimura
Keyword(s):  
Recognition System ◽  
Handwritten Numeral Recognition ◽  
Numeral Recognition

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

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.

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.

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