Feature Selection for DNN-HMM Based Mongolian Offline Handwriting Recognition

2019 ◽  
Author(s):  
Huijuan Wu ◽  
Daoerji Fan
Keyword(s):  
Feature Selection ◽  
Handwriting Recognition ◽  
Selection For ◽  
Offline Handwriting Recognition

FEATURE SELECTION FOR HMM AND BLSTM BASED HANDWRITING RECOGNITION OF WHITEBOARD NOTES

2009 ◽  
Vol 23 (05) ◽  
pp. 907-923 ◽  
Author(s):  
MARCUS LIWICKI ◽  
HORST BUNKE
Keyword(s):  
Feature Selection ◽  
Reference System ◽  
Hidden Markov ◽  
Handwriting Recognition ◽  
Statistical Significance ◽  
Forward Search ◽  
Online Handwriting Recognition ◽  
Selection Experiments ◽  
Selection For ◽  
Online Handwriting

In this paper, we describe feature selection experiments for online handwriting recognition. We investigated a set of 25 online and pseudo-offline features to find out which features are important and which features may be redundant. To analyze the saliency of the features, we applied a sequential forward and a sequential backward search on the feature set. A hidden Markov model and a neural network based recognizer have been used as recognition engines. In our experiments, we obtained interesting results. Using a set of only five features, we achieved a performance similar to that of the reference system that uses all 25 features. The five selected features have a low correlation and have been the top choices during the first iterations of the forward search with both recognizers. Furthermore, for both recognizers, subsets have been identified that outperform the reference system with statistical significance. In order to assess the results more rigorously, we have compared our recognizer with the widely used commercial recognizer from Microsoft.

A Genetic Algorithm Based Feature Selection for Handwritten Digit Recognition

2019 ◽  
Vol 12 (4) ◽  
pp. 304-316 ◽  
Author(s):  
Savita Ahlawat ◽  
Rahul Rishi
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Recognition Accuracy ◽  
Handwriting Recognition ◽  
Computational Cost ◽  
Handwritten Digit Recognition ◽  
Large Variability ◽  
Digit Recognition ◽  
Handwritten Digit ◽  
Selection For

Background: The data proliferation has been resulted in large-scale, high dimensional data and brings new challenges for feature selection in handwriting recognition problems. The practical challenges like the large variability and ambiguities present in the individual’s handwriting style demand an optimal feature selection algorithm that would be capable to enhance the recognition accuracy of handwriting recognition system with reduced training efforts and computational cost. Objective: This paper gives emphasis on the feature selection process and proposed a genetic algorithm based feature selection technique for handwritten digit recognition. Methods: A hybrid feature set of statistical and geometrical features is developed in order to get the effective feature set consist of local and global characteristics of sample digits. The method utilizes a genetic algorithm based feature selection for selecting best distinguishable features and k-nearest neighbour for evaluating the fitness of features of handwritten digit dataset. Results: The experiments are carried out on standard The Chars74K handwritten digit dataset and reported a 66% reduction in the original feature set without sacrificing the recognition accuracy. Conclusion: The experiment results show the effectiveness of the proposed approach.

Chicken Algorithm Based Feature Selection For Arabic Information Searching System

2019 ◽  
pp. 389
Author(s):  
زينب عبدالأمير ◽  
علياء كريم عبدالحسن
Keyword(s):  
Feature Selection ◽  
Information Searching ◽  
Selection For
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