학술논문

A statistical model for writer verification
Document Type
Conference
Source
Eighth International Conference on Document Analysis and Recognition (ICDAR'05) Document Analysis and Recognition Document Analysis and Recognition, 2005. Proceedings. Eighth International Conference on. :1105-1109 Vol. 2 2005
Subject
Computing and Processing
Signal Processing and Analysis
Distributed computing
Probability
Writing
Principal component analysis
Parameter estimation
Computer science
Computational modeling
Gray-scale
Entropy
Text analysis
Language
ISSN
1520-5363
2379-2140
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 labeled 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.