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.

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.

Improving Pattern Recognition Rate by Gaussian Hopfield Neural Network

2011 ◽  
Vol 189-193 ◽  
pp. 2042-2045 ◽  
Author(s):  
Shang Jen Chuang ◽  
Chiung Hsing Chen ◽  
Chien Chih Kao ◽  
Fang Tsung Liu
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Gaussian Distribution ◽  
Test Pattern ◽  
Recognition Rate ◽  
Hopfield Neural Network ◽  
Training Data ◽  
New Method ◽  
Gaussian Filter ◽  
Testing Data

English letters cannot be recognized by the Hopfield Neural Network if it contains noise over 50%. This paper proposes a new method to improve recognition rate of the Hopfield Neural Network. To advance it, we add the Gaussian distribution feature to the Hopfield Neural Network. The Gaussian filter was added to eliminate noise and improve Hopfield Neural Network’s recognition rate. We use English letters from ‘A’ to ‘Z’ as training data. The noises from 0% to 100% were generated randomly for testing data. Initially, we use the Gaussian filter to eliminate noise and then to recognize test pattern by Hopfield Neural Network. The results are we found that if letters contain noise between 50% and 53% will become reverse phenomenon or unable recognition [6]. In this paper, we propose to uses multiple filters to improve recognition rate when letters contain noise between 50% and 53%.

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

Speaker Recognition With Normal and Telephonic Assamese Speech Using I-Vector and Learning-Based Classifier

2017 ◽  
pp. 256-280
Author(s):  
Mridusmita Sharma ◽  
Rituraj Kaushik ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Neural Network ◽  
Speaker Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Feed Forward Neural Network ◽  
Individual Behaviour ◽  
Behavioural Characteristics ◽  
Regional Languages ◽  
Optimal Feature

Speaker recognition is the task of identifying a person by his/her unique identification features or behavioural characteristics that are included in the speech uttered by the person. Speaker recognition deals with the identity of the speaker. It is a biometric modality which uses the features of the speaker that is influenced by one's individual behaviour as well as the characteristics of the vocal cord. The issue becomes more complex when regional languages are considered. Here, the authors report the design of a speaker recognition system using normal and telephonic Assamese speech for their case study. In their work, the authors have implemented i-vectors as features to generate an optimal feature set and have used the Feed Forward Neural Network for the recognition purpose which gives a fairly high recognition rate.

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