The Reduction of Memory and the Improvement of Recognition Rate for HMM On-Line Handwriting Recognition

Author(s):  
A. Funada ◽  
D. Muramatsu ◽  
T. Matsumoto
Author(s):  
SIMON GÜNTER ◽  
HORST BUNKE

Handwritten text recognition is one of the most difficult problems in the field of pattern recognition. In this paper, we describe our efforts towards improving the performance of state-of-the-art handwriting recognition systems through the use of classifier ensembles. There are many examples of classification problems in the literature where multiple classifier systems increase the performance over single classifiers. Normally one of the two following approaches is used to create a multiple classifier system. (1) Several classifiers are developed completely independent of each other and combined in a last step. (2) Several classifiers are created out of one prototype classifier by using so-called classifier ensemble creation methods. In this paper an algorithm which combines both approaches is introduced and it is used to increase the recognition rate of a hidden Markov model (HMM) based handwritten word recognizer.


2013 ◽  
Vol 765-767 ◽  
pp. 2195-2198
Author(s):  
Wei Dong Xie ◽  
Kan Gao ◽  
Ji Sheng Shen

In order to meet the development of shock absorber on-line detection, a new method of indicator diagrams recognition for shock absorber based on support vector machine (SVM) is proposed. Different fault patterns of shock absorber indicator diagram are discussed, including their main causes. The recognition model is constructed each with Linear, Polynomial and Radial Basis Function (RBF) kernel function. The experimental results show that the best average recognition rate is 96.4%. This method is effective in indicator diagram fault recognition of shock absorber.


2009 ◽  
Vol 42 (12) ◽  
pp. 3278-3286 ◽  
Author(s):  
Jakob Sternby ◽  
Jonas Morwing ◽  
Jonas Andersson ◽  
Christer Friberg

Sign in / Sign up

Export Citation Format

Share Document