Handwriting recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications that includes, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. Neural Network (NN) with its inherent learning ability offers promising solutions for handwritten characte...

palmprint recognition is a new biometrics system based on physiological characteristics of the palmprint, which includes rich, stable, and unique features such as lines, points, and texture. texture is one of the most important features extracted from low resolution images. in this paper, a new local descriptor, local composition derivative pattern (lcdp) is proposed to extract smartly stronger...

Automatic pattern recognition is usually considered as an engineering area which focusses on the development and evaluation of systems that imitate or assist humans in their ability of recognizing patterns. It may, however, also be considered as a science that studies the faculty of human beings (and possibly other biological systems) to discover, distinguish, characterize patterns in their env...

In this paper we present a method for building a robust pattern recognition system by integrating diierent classiiers making use of diverse features. In practical applications, individual classiiers may have various limitations when they are used alone, but their performance can be improved signiicantly if we combine the complementary advantages of the classiiers to deal with many diierent feat...

This report presents a synoptic overview of Thue Systems. Thue Systems were introduced in the early 1900s by the Norwegian mathematician and logician Axel Thue. In this report the author suggests ways in which such systems can be used in pattern recognition.

In pattern recognition, learning, and data mining one obtains information from information-carrying objects. This involves an objective definition of the information in a single object, the information to go from one object to another object in a pair of objects, the information to go from one object to any other object in a multiple of objects, and the shared information between objects. This ...

Multiple classifiers combination is a technique that combines the decisions of different classifiers as to reduce the variance of estimation errors and improve the overall classification accuracy. A new multiple classifiers fusion method integrated classifier selection and classifier combination is proposed in this paper. It is base on interval-valued fuzzy permutation. Firstly, normalize all c...

Different pattern recognition techniques based on correlation operations are reviewed. The method is applied to conventional images and range images (with tridimensional information). The nonlinear correlations applied to bidimensional images are based on binary decompositions. We study the noise robustness when images are corrupted with substitutive Gaussian noise. Combining the correlation wi...

Abstract: Methods for solution of a large class of problems on the base of nonclassical, multiple-valued, and probabilistic logics have been discussed. A theory of knowledge about changing knowledge, of defeasible inference, and network approach to an analogous derivation have been suggested. A method for regularity search, logic-axiomatic and logic-probabilistic methods for learning of terms a...

Several researchers have experimentally shown that substantial improvements can be obtained in diicult pattern recognition problems by combining or integrating the outputs of multiple classiiers. This paper provides an analytical framework to quantify the improvements in classiication results due to combining. The results apply to both linear combiners and the order statistics combiners introdu...