Exploiting State-of-the-Art Deep Learning Methods for Document Image Analysis

被引：7

作者：

Pondenkandath, Vinaychandran ^{[1
,2
]}

Seuret, Mathias ^{[2
]}

Ingold, Rolf ^{[2
]}

Afzal, Muhammad Zeshan ^{[1
]}

Liwicki, Marcus ^{[1
,2
]}

机构：

[1] TU Kaiserslautern, MindGarage, Kaiserslautern, Germany

[2] Univ Fribourg, DIVA, Fribourg, Switzerland

来源：

2017 14TH IAPR INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR 2017), VOL 5 | 2017年

基金：

瑞士国家科学基金会;

关键词：

RECOGNITION;

D O I：

10.1109/ICDAR.2017.325

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper provides details of our (partially awardwinning) methods submitted to four competitions of ICDAR 2017. In particular, they are designed to (i) classify scripts, (ii) perform pixel-based labeling for layout analysis, (iii) identify writers, and (iv) recognize font size and types. The methods build on the current state-of-the-art in Deep Learning and have been adapted to the specific needs of the individual tasks. All methods are variants of Convolutional Neural Network (CNN) with specialized architectures, initialization, and other tricks which have been introduced in the field of deep learning within the last few years.

引用

页码：30 / 35

页数：6

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