Off-line writer identification using multi-scale local binary patterns and SR-KDA

E. Khalifa, Somaya Al-Maadeed, Muhammad Tahir, Fouad Khelifi, Ahmed Bouridane

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Citation (Scopus)


Writer identification is becoming an increasingly important research topic especially in forensic and biometric applications. This paper presents a novel method for performing offline write identification by using multi-scale local binary patterns histogram (MLBPH) features. The proposed feature (MLBPH) when combined with edge-hinge based feature achieves a top 1 recognition rate of 92% on the benchmark IAM English handwriting dataset, outperforming current state of the art features. Further, kernel discriminant analysis using spectral regression (SR-KDA) is introduced as dimensionality reduction technique to avoid the overfitting problem associated with using multi-scale data.
Original languageEnglish
Title of host publication2013 25th International Conference on Microelectronics (ICM)
Place of PublicationPiscataway, NJ
ISBN (Print)978-1-4799-3569-7
Publication statusPublished - 2013


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