Text-independent chinese writer identification using hybrid slt-lbp feature | [Journal of Theoretical and Applied Information Technology • 2020]

Author(s):

1. GLORIA JENIS TAN: Centre for IT Development and Services,Universiti Malaysia Sarawak (UNIMAS), Sarawak,Malaysia

2. ROSELY KUMOI: Faculty of Engineering, School of Computing,Universiti Teknologi Malaysia (UTM), Johor,Malaysia

3. MOHD SHAFRY MOHD RAHIM: Centre for IT Development and Services,Universiti Malaysia Sarawak (UNIMAS), Sarawak,Malaysia

4. TAN CHI WEE: Faculty of Computing and Information Technology, Tunku Abdul Rahman University College, Malaysia.

5. GHAZALI SULONG: Management & Science University (MSU), Malaysia

Abstract:

This study proposes a new hybrid method using texture features of input handwriting document image as global to overcome the limitation of data heterogeneity, which causing the ambiguity and leads to inconsistent results apart from problems of scale involve database size. The method first adopts Slantlet Transform (SLT) to bring out hidden texture details prior to feature extractions. Then, Local Binary Pattern (LBP) descriptor is applied on the SLT image to extract texture features. A new hybrid method Slantlet Transform based Local Binary Pattern (SLT-LBP), are experimented on an open and widely used HIT-MW Chinese database for performance evaluation. This study strengthens the idea that to unravel some of data heterogeneity and lead to improve identification performance, especially searching for relevant document from large complex repositories is an essential issue.

Page(s): 1322-1332

DOI: DOI not available

Published: Journal: Journal of Theoretical and Applied Information Technology, Volume: 98, Issue: 9, Year: 2020

Keywords:

Local Binary Pattern , Chinese Handwriting , texture , textindependent , Slantlet Transform , Writer Identification

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