Sparse-parametric writer identification using heterogeneous feature groups

L Schomaker*, M Bulacu, M van Erp

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

22 Citations (Scopus)

Abstract

This paper evaluates the performance of edge-based directional probability distributions as features in writer identification in comparison to a number of non-angular features. It is noted that angular features outperform all other features. However, the non-angular features provide additional valuable information. Rank-combination was used to realize a sparse-parametric combination scheme based on nearest-neighbor search. Limitations of the proposed methods pertain to the amount of handwritten material needed in order to obtain reliable distribution estimates. The global features treated in this study are sensitive to major style variation (upper- vs lower case), slant, and forged styles, which necessitates the use of other features in realistic forensic writer identification procedures.

Original languageEnglish
Title of host publication2003 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL 1, PROCEEDINGS
Place of PublicationNEW YORK
PublisherIEEE (The Institute of Electrical and Electronics Engineers)
Pages545-548
Number of pages4
ISBN (Print)0-7803-7750-8
Publication statusPublished - 2003
EventIEEE International Conference on Image Processing - , Spain
Duration: 14-Sep-200317-Sep-2003

Publication series

NameIEEE International Conference on Image Processing (ICIP)
PublisherIEEE
ISSN (Print)1522-4880

Other

OtherIEEE International Conference on Image Processing
CountrySpain
Period14/09/200317/09/2003

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