Paper Info
Title
A Statistical Model For Writer Verification
Abstract
A statistical model for determining whether a pair of documents, a known and a questioned, were written by the same individual is proposed. The model has the following four components: (i) discriminating elements, e.g., global features and characters, are extracted from each document, (ii) differences between corresponding elements from each document are computed, (iii) using conditional probability estimates of each difference, the log-likelihood ratio (LLR) is computed for the hypotheses that the documents were written by the same or different writers; the conditional probability estimates themselves are determined from labelled samples using either Gaussian or gamma estimates for the differences assuming their statistical independence, and (iv) distributions of the LLRs for same and different writer LLRs are analyzed to calibrate the strength of evidence into a standard nine-point scale used by questioned document examiners. The model is illustrated with experimental results for a specific set of discriminating elements.
Year
DOI
Venue
2005
10.1109/ICDAR.2005.33
ICDAR-1
Keywords
Field
DocType
corresponding element,different writer,conditional probability,statistical model,document examiner,gamma estimate,statistical independence,different writer llrs,writer verification,conditional probability estimate,log likelihood ratio,handwriting recognition,statistical analysis
Conditional probability,Pattern recognition,Computer science,Document image processing,Handwriting recognition,Gaussian,Artificial intelligence,Statistical model,Independence (probability theory),Statistical analysis
Conference
ISSN
ISBN
Citations 
1520-5363
0-7695-2420-6
18
PageRank 
References 
Authors
1.25
1
5
Name
Order
Citations
PageRank
Sargur N. Srihari12949685.29
Matthew J. Beal260064.31
kamaiah bandi3181.25
vallabh o shah4181.25
Prashant Krishnamurthy51222104.71