Individuality of Handwritten Characters
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Text-Independent Writer Identification and Verification Using Textural and Allographic Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
A writer identification system for on-line whiteboard data
Pattern Recognition
Latent Dirichlet allocation based writer identification in offline handwriting
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Repudiation detection in handwritten documents
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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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.