Character recognition is the important area in image processing and pattern recognition fields. Handwritten character recognition has received extensive attention in academic and production fields. The recognition sys...
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ISBN:
(纸本)9781424429271
Character recognition is the important area in image processing and pattern recognition fields. Handwritten character recognition has received extensive attention in academic and production fields. The recognition system can be either on-line or off-line. Off-line handwriting recognition is the subfield of optical character recognition. India is a multi-lingual and multi-script country, where eighteen official scripts are accepted and have over hundred regional languages. In this paper we present Zone and Distance metric based featureextraction system. The character centroid is computed and the image is further divided in to n equal zones. Average distance from the character centroid to the each pixel present in the zone is computed. This procedure is repeated for all the zones present in the numeral image. Finally n such features are extracted for classification and recognition. Support vector machine is used for subsequent classification and recognition purpose. We obtained 97.75% recognition rate for Kannada numerals.
In this paper, we propose an algorithm of featureextraction for the composite surface triangle mesh from the stereo lithography (STL) file. By creating a database for quickly determining the dihedral angle threshold ...
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ISBN:
(纸本)9781424441983
In this paper, we propose an algorithm of featureextraction for the composite surface triangle mesh from the stereo lithography (STL) file. By creating a database for quickly determining the dihedral angle threshold of the composite surface mesh, we give a simple feature extraction algorithm based on the dihedral angle threshold. Furthermore, combining with other techniques such as perimeter ratio and principal curvatures, we get an improved feature extraction algorithm for the composite surface triangle mesh. Numerical experiments demonstrate that this algorithm is robust and effective.
Adaptive featureextraction is useful in many information processing systems. In this paper we propose a learning machine implemented via a neural network to perform such a task using the tool principal component anal...
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Adaptive featureextraction is useful in many information processing systems. In this paper we propose a learning machine implemented via a neural network to perform such a task using the tool principal component analysis. This machine (1) is adaptive to nonstationary input, (2) is based on an un-supervised learning concept and requires no knowledge of if, or when, the input changes statistically, and (3) performs on-line computation that requires little memory or data storage. Associated with this machine, we propose a learning algorithm (LEAP), whose convergence properties are theoretically analyzed and whose performance is evaluated via computer simulations. Two major contributions of this paper are: (1) Under appropriate conditions, we prove that the algorithm will extract multiple principal components, when the learning rate is constant;and (2) we identify a near optimal domain of attraction.
We investigate the asymptotic behavior of a general class of on-line Principal Component Analysis (PCA) learning algorithms, focusing our attention on the analysis of two algorithms which have recently been proposed a...
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We investigate the asymptotic behavior of a general class of on-line Principal Component Analysis (PCA) learning algorithms, focusing our attention on the analysis of two algorithms which have recently been proposed and are based on strictly local learning rules. We rigorously establish that the behavior of the algorithms is intimately related to an ordinary differential equation (ODE) which is obtained by suitably averaging over the training patterns, and study the equilibria of these ODEs and their local stability properties. Our results imply, in particular, that local PCA algorithms should always incorporate hierarchical rather than more competitive, symmetric decorrelation, for reasons of superior performance of the algorithms.
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