Title
Text-independent writer identification using SIFT descriptor and contour-directional feature
Abstract
This paper presents a method for text-independent writer identification using SIFT descriptor and contour-directional feature (CDF). The proposed method contains two stages. In the first stage, a codebook of local texture patterns is constructed by clustering a set of SIFT descriptors extracted from images. Using this codebook, the occurrence histograms are calculated to determine the similarities between different images. For each image, we obtain a candidate list of reference images. The next stage is to refine the candidate list using the contour-directional feature and SIFT descriptor. The proposed method is evaluated with two datasets: the ICFHR2012-Latin dataset and the ICDAR2013 dataset. Experimental results show that the proposed method outperforms the state-of-the-art algorithms and archives the best performance.
Year
DOI
Venue
2015
10.1109/ICDAR.2015.7333732
International Conference on Document Analysis and Recognition
Keywords
Field
DocType
Writer identification, text-independent, codebook, SIFT descriptor and contour-directional feature
Histogram,Computer vision,Scale-invariant feature transform,GLOH,Pattern recognition,Computer science,Artificial intelligence,Cluster analysis,Codebook
Conference
ISSN
Citations 
PageRank 
1520-5363
10
0.48
References 
Authors
11
4
Name
Order
Citations
PageRank
Yu-Jie Xiong1141.21
Ying Wen2225.85
patrick s p wang330347.66
Yue Lu443427.43