Wednesday, August 16, 2006
[內容整理] FEATURE EXTRACTION METHODS
《FEATURE EXTRACTION METHODS FOR CHARACTER RECOGNITION--A SURVEY》 (1995)
Abstract
This paper presents an overview of feature extraction methods for off-line recognition of
segmented (isolated) characters.
Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance in character recognition systems. Different feature extraction methods are designed for different representations of the characters, such as solid binary characters, character contours, skeletons (thinned characters) or gray-level subimages of each individual character.
The feature extraction methods are discussed in terms of invariance properties, reconstructability and expected distortions and variability of the characters. The problem of choosing the appropriate feature extraction method for a given application is also discussed. When a few promising feature extraction methods have been identified, they need to be evaluated experimentally to find the best method for the given application.
* Different feature types may need different types of classifiers.
* ["特徵截取"的定義] Devijver and Kittler define feature extraction [page 12 in reference (11)] as the problem of"extracting from the raw data the information which is most relevant for classification purposes, in the sense of minimizing the within-class pattern variability while enhancing the between-classs pattern variability".
(就像之前有篇paper說的, 選用的特徵值, 必須使同一個使用者的每個文字間的差異
達到最小, 但不同使用者的文字間要有明顯差異, 這樣才能區分不同的人寫的字)
* 一個好的 feature extraction method 對你將應用到的地方是很重要的.
* Also, more than one pattern class may be necessary to characterize characters
that can be written in two or more distinct ways. (要考慮到一個字元的不同寫法)
Abstract
This paper presents an overview of feature extraction methods for off-line recognition of
segmented (isolated) characters.
Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance in character recognition systems. Different feature extraction methods are designed for different representations of the characters, such as solid binary characters, character contours, skeletons (thinned characters) or gray-level subimages of each individual character.
The feature extraction methods are discussed in terms of invariance properties, reconstructability and expected distortions and variability of the characters. The problem of choosing the appropriate feature extraction method for a given application is also discussed. When a few promising feature extraction methods have been identified, they need to be evaluated experimentally to find the best method for the given application.
* Different feature types may need different types of classifiers.
* ["特徵截取"的定義] Devijver and Kittler define feature extraction [page 12 in reference (11)] as the problem of"extracting from the raw data the information which is most relevant for classification purposes, in the sense of minimizing the within-class pattern variability while enhancing the between-classs pattern variability".
(就像之前有篇paper說的, 選用的特徵值, 必須使同一個使用者的每個文字間的差異
達到最小, 但不同使用者的文字間要有明顯差異, 這樣才能區分不同的人寫的字)
* 一個好的 feature extraction method 對你將應用到的地方是很重要的.
* Also, more than one pattern class may be necessary to characterize characters
that can be written in two or more distinct ways. (要考慮到一個字元的不同寫法)
===> posted by BASSHOU @ 2:08 AM 
