Object Recognition Using Summed Features Classifier

被引：0

作者：

Lindner, Marcus ^{[1
]}

Block, Marco ^{[1
]}

Rojas, Raul ^{[1
]}

机构：

[1] Free Univ Berlin, Inst Informat & Math, D-14195 Berlin, Germany

来源：

ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, PT I | 2012年 / 7267卷

关键词：

IMAGE; ALGORITHMS; MODEL;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A common task in the field of document digitization for information retrieval is separating text and non-text elements. In this paper an innovative approach of recognizing patterns is presented. Statistical and structural features in arbitrary number are combined into a rating tree, which is an adapted decision tree. Such a tree is trained for character patterns to distinguish text elements from non-text elements. First experiments in a binarization application have shown promising results in significant reduction of false-positives without producing false-negatives.

引用

页码：543 / 550

页数：8

共 24 条

[1] Ahmad AT, 2008, INFORM-J COMPUT INFO, V32, P275
[2] Al-Taani A.T., 2005, INT J COMPUTATIONAL, V2
[3] An introduction to MCMC for machine learning
Andrieu, C
de Freitas, N
Doucet, A
Jordan, MI
[J]. MACHINE LEARNING, 2003, 50 (1-2) : 5 - 43
[4] [Anonymous], 2006, 2006 IEEE COMP SOC C
[5] [Anonymous], 1993, CRGTR931 U TOR DEP C
[6] [Anonymous], 2008, Monte Carlo Methods
[7] Bajramovic F, 2006, LECT NOTES COMPUT SC, V4179, P1186
[8] Behnke S, 1997, 1997 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, P1391, DOI 10.1109/ICNN.1997.613997
[9] Block M, 2009, THIRD INTERNATIONAL CONFERENCE ON DIGITAL SOCIETY: ICDS 2009, PROCEEDINGS, P294, DOI 10.1109/ICDS.2009.45
[10] Boiman O., 2008, COMPUTER VISION PATT, P1, DOI DOI 10.1109/CVPR.2008.4587598

← 1 2 3 →