Fast Rule-line Removal using Integral Images and Support Vector Machines

被引：8
|
作者：
Kumar, Jayant [1 ]
Doermann, David [1 ]
机构：
[1] Univ Maryland, Inst Adv Comp Studies, College Pk, MD 20742 USA
来源：
11TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR 2011) | 2011年
关键词：
Rule-line; Handwritten Documents; Arabic;
D O I：
10.1109/ICDAR.2011.123
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we present a fast and effective method for removing pre-printed rule-lines in handwritten document images. We use an integral-image representation which allows fast computation of features and apply techniques for large scale Support Vector learning using a data selection strategy to sample a small subset of training data. Results on both constructed and real-world data sets show that the method is effective for rule-line removal. We compare our method to a subspace-based method and show that better accuracy can be achieved in considerably less time. The integral-image based features proposed in the paper are generic and can be applied to other problems as well.
引用
收藏
页码：584 / 588
页数：5
相关论文
共 50 条
  • [21] Support Vector Machines and the Bayes Rule in Classification
    Yi Lin
    Data Mining and Knowledge Discovery, 2002, 6 : 259 - 275
  • [22] Combining multiple support vector machines using fuzzy integral for classification
    Yan, Gen-Ting
    Ma, Guang-Fu
    Zhu, Liang-Kuan
    Shi, Zhong
    PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2006, : 3438 - +
  • [23] Obscure bleeding detection in endoscopy images using support vector machines
    Liu, Jianguo
    Yuan, Xiaohui
    OPTIMIZATION AND ENGINEERING, 2009, 10 (02) : 289 - 299
  • [24] Enhancement of the Classification of Multichannel Chromosome Images Using Support Vector Machines
    Karvelis, P. S.
    Fotiadis, D. I.
    Georgiou, I.
    Sakaloglou, P.
    2009 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-20, 2009, : 3601 - 3604
  • [25] Classification of hyperspectral endocrine tissue images using support vector machines
    Maktabi, Marianne
    Koehler, Hannes
    Ivanova, Magarita
    Neumuth, Thomas
    Rayes, Nada
    Seidemann, Lena
    Sucher, Robert
    Jansen-Winkeln, Boris
    Gockel, Ines
    Barberio, Manuel
    Chalopin, Claire
    INTERNATIONAL JOURNAL OF MEDICAL ROBOTICS AND COMPUTER ASSISTED SURGERY, 2020, 16 (05): : 1 - 10
  • [26] Obscure bleeding detection in endoscopy images using support vector machines
    Jianguo Liu
    Xiaohui Yuan
    Optimization and Engineering, 2009, 10 : 289 - 299
  • [27] I semantic extraction of the building images using support vector machines
    Wang, YN
    Chen, LB
    Hu, BG
    2002 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-4, PROCEEDINGS, 2002, : 1608 - 1613
  • [28] Geological Units Classification of Multispectral Images by Using Support Vector Machines
    Kovacevic, Milos
    Bajat, Branislav
    Trivic, Branislav
    Pavlovic, Radmila
    2009 INTERNATIONAL CONFERENCE ON INTELLIGENT NETWORKING AND COLLABORATIVE SYSTEMS (INCOS 2009), 2009, : 267 - +
  • [29] Morphological classification of medical images using nonlinear support vector machines
    Davatzikos, C
    Shen, DG
    Lao, ZQ
    Xue, Z
    Karacali, B
    2004 2ND IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: MACRO TO NANO, VOLS 1 and 2, 2004, : 587 - 590
  • [30] Classification of Hyperspectral Images with Support Vector Machines
    Andreola, Rafaela
    Haertel, Vitor
    BOLETIM DE CIENCIAS GEODESICAS, 2010, 16 (02): : 210 - 231
12345