Insights on the Use of Convolutional Neural Networks for Document Image Binarization

被引：0

作者：

Pastor-Pellicer, J. ^{[1
]}

Espana-Boquera, S. ^{[1
]}

Zamora-Martinez, F. ^{[2
]}

Afzal, M. Zeshan ^{[3
]}

Jose Castro-Bleda, Maria ^{[1
]}

机构：

[1] Univ Politecn Valencia, Dept Sistemas Informat & Computac, E-46022 Valencia, Spain

[2] Univ CEU Cardenal Herrera, Dept Ciencias Fis Math & Computac, Valencia, Spain

[3] German Res Ctr Artificial Intelligence DFKI, Kaiserslautern, Germany

来源：

ADVANCES IN COMPUTATIONAL INTELLIGENCE, PT II | 2015年 / 9095卷

关键词：

D O I：

10.1007/978-3-319-19222-2_10

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Convolutional Neural Networks have systematically shown good performance in Computer Vision and in Handwritten Text Recognition tasks. This paper proposes the use of these models for document image binarization. The main idea is to classify each pixel of the image into foreground and background from a sliding window centered at the pixel to be classified. An experimental analysis on the effect of sensitive parameters and some working topologies are proposed using two different corpora, of very different properties: DIBCO and Santgall.

引用

页码：115 / 126

页数：12

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