Paper Info
Title
Using top n Recognition Candidates to Categorize On-line Handwritten Documents
Abstract
The traditional weighting schemes used in text categorization for the vector space model (VSM) cannot exploit information intrinsic to texts obtained through on-line handwriting recognition or any OCR process. Especially, top n (n 1) recognition candidates could not be used without flooding the resulting text with false occurrences of spurious terms. In this paper, an improved weighting scheme for text categorization, that estimates the occurrences of terms from the posterior probabilities of the top n candidates, is proposed. The experimental results show that the categorization performances increase for texts with high error rates.
Year
DOI
Venue
2009
10.1109/ICDAR.2009.137
ICDAR-1
Keywords
Field
DocType
document image processing,handwritten character recognition,optical character recognition,probability,text analysis,OCR process,handwriting recognition,online handwritten document categorization,posterior probability,recognition candidate,text categorization,vector space model,noisy text categorization,recognition candidates,weighting scheme
Categorization,Weighting,Pattern recognition,Noise measurement,Computer science,Support vector machine,Handwriting recognition,Optical character recognition,Posterior probability,Natural language processing,Artificial intelligence,Vector space model
Conference
Citations 
PageRank 
References 
0
0.34
11
Authors
3
Name
Order
Citations
PageRank
Sebastián Peña Saldarriaga1154.06
Emmanuel Morin24216.13
Christian Viard-Gaudin344446.20