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Y. X. Gu

dblp:63/9703 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1983
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Image recognition and object detection · 87% Kernel, tree and ensemble methods · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Image recognition and object detection
character recognition
0.011983
Application of a Multilayer Decision Tree in Computer Recognition of Chinese Characters · IEEE Trans. Pattern Anal. Mach. Intell. 1983
Computer vision › Image recognition and object detection › character recognition
chinese character recognition
0.011983
Application of a Multilayer Decision Tree in Computer Recognition of Chinese Characters · IEEE Trans. Pattern Anal. Mach. Intell. 1983
Machine learning › Kernel, tree and ensemble methods › classifier combination
multi-stage classification
0.011983
Application of a Multilayer Decision Tree in Computer Recognition of Chinese Characters · IEEE Trans. Pattern Anal. Mach. Intell. 1983

Methods — techniques the papers use, named apart from their topics

walsh coefficients · 0.0multilayer decision tree · 0.0character alignment · 0.0
YearPublicationVenuePosition
1983 Application of a Multilayer Decision Tree in Computer Recognition of Chinese Characters
abstract
A multistage classifier with general tree structure has been developed to recognize a large number of Chinese characters. A simple and efficient method of classifying the characters was achieved by choosing the best feature at each stage of the tree. The features used are Walsh coefficients obtained from two profiles of a character projected onto the X-Y orthogonal axes. Some algorithms for aligning the characters were compared and one of them was adopted in this recognition scheme. A high recognition rate of about 99.5 percent was obtained in an experiment with more than 3000 different Chinese characters.
Y. X. Gu, Qing Ren Wang, Ching Y. Suen
IEEE Trans. Pattern Anal. Mach. Intell.1