Automatic Parameter Tuning of K-Means Algorithm for Document Binarization

被引：6
|
作者：
Gattal, Abdeljalil [1 ]
Abbas, Faycel [2 ]
Laouar, Mohamed Ridda [1 ]
机构：
[1] Larbi Tebessi Univ, Dept Math & Comp Sci, Tebessa, Algeria
[2] Bouira Univ, Fac Sci & Appl Sci, Comp Sci Dept, LIMPAF Lab, Bouira, Algeria
来源：
PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND NEW TECHNOLOGIES (ICSENT '18) | 2018年
关键词：
Document Binarization; K-Means algorithm; Automatic parameter tuning; H-DIBCO; 2016; dataset;
D O I：
10.1145/3330089.3330124
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
The document binarization is a primary processing step toward document recognition system. It goals to separate the foreground from the document background. In this paper, we propose an algorithm for the binarization of document images degraded by using the clustering algorithm K-Means with automatic parameter tuning. It uses the K-Means algorithm to classify the document image into three classes as background, foreground and noise labels. Experimental results show that our method is more robust to the state of the art on recent benchmarks on the H-DIBCO 2016 dataset.
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页数：4
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