Writer identification using texture features: A comparative study. (October 2018)

• Record Type: Journal Article
• Authors: Singh, Priyanka; Roy, Partha Pratim; Raman, Balasubramanian
• Is Part Of: Computers & electrical engineering. Volume 71(2018)
• Page Start: 1
• Page End: 12
• Publication Date: 2018-10
• Subjects: Texture-based writer identification -- Texture descriptors -- Devnagri scripts -- Handwritten documents; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.07.003
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A texture based approach for writer identification of multiple scripts on a single platform has been compared in this paper. It analyzes six texture descriptors namely LBP, LPQ, DWT+LEP, DWT+DLEP, CSLBCoP and LTriDP for identifying the writer. The comparative study has been performed for Latin, Arabic and Devnagri databases on a uniform platform where the Devnagri database is contributed by us towards this end. Abstract: A texture-based approach for writer identification of multiple scripts on a single platform is presented in this paper. Potential texture descriptors, namely Local Binary Pattern (LBP), Local Phase Quantization (LPQ), Discrete Wavelet Transform-based Local Extrema Pattern (DWT+LEP), Discrete Wavelet Transform-based Directional and Local Extrema Pattern (DWT+DLEP), Center Symmetric Local Binary Co-occurrence Pattern (CSLBCoP), and Local Tri-Directional Pattern (LTriDP) have been analyzed for identifying the writers. Comparative study for Latin, Arabic, and Devnagri databases was performed, with the Devnagri database contributed by us. The study shows high writer identification rates of 97.62% for IAM dataset using LBP features and Support Vector Machine (SVM) classifier, 95.60% for KHATT database using k-Nearest Neighbor (kNN), and 65.80% for Devnagri scripts using LPQ features and kNN classifier.