Linked Life Data

15719928

Source:http://linkedlifedata.com/resource/pubmed/id/15719928

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2005-2-21
pubmed:abstractText
Merged characters are the major cause of recognition errors. We classify the merging relationship between two involved characters into three types: "linear," "nonlinear," and "overlapped." Most segmentation methods handle the first type well, however, their capabilities of handling the other two types are limited. The weakness of handling the nonlinear and overlapped types results from character segmentation by linear, usually vertical, cuts assumed in these methods. This paper proposes a novel merged character segmentation and recognition method based on forepart prediction, necessity-sufficiency matching and character-adaptive masking. This method utilizes the information obtained from the forepart of merged characters to predict candidates for the leftmost character, and then applies character-adaptive masking and character recognition to verifying the prediction. Therefore, the arbitrary-shaped cutting path will follow the right shape of the leftmost character so as to preserve the shape of the next character. This method handles the first two types well and greatly improves the segmentation accuracy of the overlapped type. The experimental results and the performance comparisons with other methods demonstrate the effectiveness of the proposed method.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
1083-4419
pubmed:author
pubmed:issnType
Print
pubmed:volume
35
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2-11
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Recognition of merged characters based on forepart prediction, necessity-sufficiency matching, and character-adaptive masking.
pubmed:affiliation
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong. songjq@ieee.org
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't, Evaluation Studies