Paper Info
Title
Semi-automatic Tibetan Component Annotation from Online Handwritten Tibetan Character Database by Optimizing Segmentation Hypotheses
Abstract
One of important steps in hybrid statistical-structural recognition method for handwritten characters is to label primitives for classifier training and label structural position information for structural recognition. In this paper, we propose a semi-automatic component (primitive) annotation method for online handwritten Tibetan character database. All samples of each character class are over-segmented into sub-structure block sequences. We select correct segmentation points from one of segmented character samples and get component templates of this character class. Other samples of the same character class with sub-structure block sequences are matched with the component templates by optimizing segmentation hypotheses strategy. Character samples segmented by error are re-annotated with minimal human effort at semi-automatic re-annotation module. At last we measure the performance of our component-based recognition method on the character database with component annotation for reference.
Year
DOI
Venue
2013
10.1109/ICDAR.2013.271
ICDAR-1
Keywords
Field
DocType
optimizing segmentation hypotheses,component template,sub-structure block sequence,semi-automatic component,character sample,online handwritten tibetan character,component annotation,segmented character sample,handwritten character,character database,character class,semi-automatic tibetan component annotation,image segmentation,natural language processing,handwriting recognition
Scale-space segmentation,Computer science,Handwriting recognition,Image segmentation,Artificial intelligence,Classifier (linguistics),Intelligent word recognition,Computer vision,Annotation,Intelligent character recognition,Pattern recognition,Segmentation,Database
Conference
ISSN
Citations 
PageRank 
1520-5363
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Long-long Ma1265.72
Jian Wu21307.37