A noise-adaptive discriminant function and its application to blurred machine-printed kanji recognition

Shi N.Ichiro Omachi

doi:10.1109/34.841761

A noise-adaptive discriminant function and its application to blurred machine-printed kanji recognition

Shi N.Ichiro Omachi

ENG - Communications Engineering

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

Abstract

Accurate recognition of blurred images is a practical but previously to mostly overlooked problem. In this paper, we quantify the level of noise in blurred images and propose a new modification of discriminant functions that adapts to the level of noise. Experimental results indicate that the proposed method actually enhances the existing statistical methods and has impressive ability to recognize blurred image patterns.

Original language	English
Pages (from-to)	314-319
Number of pages	6
Journal	IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume	22
Issue number	3
DOIs	https://doi.org/10.1109/34.841761
Publication status	Published - 2000

Keywords

Bayes classifier
Blurred character recognition
Discriminant function
Distribution of feature vectors
Mahalanobis distance
Noise

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10.1109/34.841761

Cite this

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abstract = "Accurate recognition of blurred images is a practical but previously to mostly overlooked problem. In this paper, we quantify the level of noise in blurred images and propose a new modification of discriminant functions that adapts to the level of noise. Experimental results indicate that the proposed method actually enhances the existing statistical methods and has impressive ability to recognize blurred image patterns.",

keywords = "Bayes classifier, Blurred character recognition, Discriminant function, Distribution of feature vectors, Mahalanobis distance, Noise",

author = "Omachi, {Shi N.Ichiro}",

note = "Funding Information: This research was partially supported by the Ministry of Education, Science, Sports, and Culture, Grant-in-Aid for Encouragement of Young Scientists, 10780216, 1998. A part of this paper appeared in [28].",

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AU - Omachi, Shi N.Ichiro

N1 - Funding Information: This research was partially supported by the Ministry of Education, Science, Sports, and Culture, Grant-in-Aid for Encouragement of Young Scientists, 10780216, 1998. A part of this paper appeared in [28].

PY - 2000

Y1 - 2000

N2 - Accurate recognition of blurred images is a practical but previously to mostly overlooked problem. In this paper, we quantify the level of noise in blurred images and propose a new modification of discriminant functions that adapts to the level of noise. Experimental results indicate that the proposed method actually enhances the existing statistical methods and has impressive ability to recognize blurred image patterns.

AB - Accurate recognition of blurred images is a practical but previously to mostly overlooked problem. In this paper, we quantify the level of noise in blurred images and propose a new modification of discriminant functions that adapts to the level of noise. Experimental results indicate that the proposed method actually enhances the existing statistical methods and has impressive ability to recognize blurred image patterns.

KW - Bayes classifier

KW - Blurred character recognition

KW - Discriminant function

KW - Distribution of feature vectors

KW - Mahalanobis distance

KW - Noise

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VL - 22

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EP - 319

JO - IEEE Transactions on Pattern Analysis and Machine Intelligence

JF - IEEE Transactions on Pattern Analysis and Machine Intelligence

IS - 3

ER -

A noise-adaptive discriminant function and its application to blurred machine-printed kanji recognition

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Keywords

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