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.
引用
收藏
页数：4
相关论文
共 50 条
  • [11] Improved Document Clustering using K-means Algorithm
    Bide, Pramod
    Shedge, Rajashree
    2015 IEEE INTERNATIONAL CONFERENCE ON ELECTRICAL, COMPUTER AND COMMUNICATION TECHNOLOGIES, 2015,
  • [12] Document Clustering - A Feasible Demonstration with K-means Algorithm
    Arif, Wajiha
    Mahoto, Naeem Ahmed
    2019 2ND INTERNATIONAL CONFERENCE ON COMPUTING, MATHEMATICS AND ENGINEERING TECHNOLOGIES (ICOMET), 2019,
  • [13] An Improved Hierarchical K-Means Algorithm for Web Document Clustering
    Liu, Yongxin
    Liu, Zhijng
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INFORMATION TECHNOLOGY, 2008, : 606 - 610
  • [14] An Approach for Document Clustering using PSO and K-means Algorithm
    Chouhan, Rashmi
    Purohit, Anuradha
    PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON INVENTIVE SYSTEMS AND CONTROL (ICISC 2018), 2018, : 1380 - 1384
  • [15] A Parameter-free Clustering Algorithm based K-means
    Slaoui, Said
    Dafir, Zineb
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2021, 12 (03) : 612 - 619
  • [16] An Enhancement in K-means Algorithm for Automatic Ultrasound Image Segmentation
    Panigrahi, Lipismita
    Panigrahi, Raghab Ranjan
    BIOMEDICAL ENGINEERING SCIENCE AND TECHNOLOGY, ICBEST 2023, 2024, 2003 : 1 - 8
  • [17] Application of K-Means Clustering Algorithm in Automatic Machine Learning
    Ji, Dongri (jidongri0016@163.com), 1600, Springer Science and Business Media Deutschland GmbH (1131):
  • [18] A Modified K-means Algorithm - Two-Layer K-means Algorithm
    Liu, Chen-Chung
    Chu, Shao-Wei
    Chan, Yung-Kuan
    Yu, Shyr-Shen
    2014 TENTH INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION HIDING AND MULTIMEDIA SIGNAL PROCESSING (IIH-MSP 2014), 2014, : 447 - 450
  • [19] Research on k-means Clustering Algorithm An Improved k-means Clustering Algorithm
    Shi Na
    Liu Xumin
    Guan Yong
    2010 THIRD INTERNATIONAL SYMPOSIUM ON INTELLIGENT INFORMATION TECHNOLOGY AND SECURITY INFORMATICS (IITSI 2010), 2010, : 63 - 67
  • [20] K and starting means for k-means algorithm
    Fahim, Ahmed
    JOURNAL OF COMPUTATIONAL SCIENCE, 2021, 55
12345