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A Weight-Selection Strategy on Training Deep Neural Networks for Imbalanced Classification Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Feature Learning for Footnote-Based Document Image Classification

Authors : Sherif Abuelwafa, Mohamed Mhiri, Rachid Hedjam, Sara Zhalehpour, Andrew Piper, Chad Wellmon, Mohamed Cheriet

Published in: Image Analysis and Recognition

Publisher: Springer International Publishing

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Abstract

Classifying document images is a challenging problem that is confronted by many obstacles; specifically, the pivotal need of hand-designed features and the scarcity of labeled data. In this paper, a new approach for classifying document images, based on the availability of footnotes in them, is presented. Our proposed approach depends mainly on a Deep Belief Network (DBN) that consists of two phases, unsupervised pre-training and supervised fine-tuning. The main advantage of using this approach is its capability to automatically engineer the best features to be extracted from a raw document image for the sake of generating an efficient representation of it. This feature learning approach takes advantage of the vast amount of available unlabeled data and employs it with the limited number of labeled data. The obtained results show that the proposed approach provides an effective document image classification framework with a highly reliable performance.

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previous chapter Footnote-Based Document Image Classification
next chapter Analysis of Sloshing in Tanks Using Image Processing
Footnotes
2
Considering having real values at the visible nodes (input document image).
 
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Metadata
Title
Feature Learning for Footnote-Based Document Image Classification
Authors
Sherif Abuelwafa
Mohamed Mhiri
Rachid Hedjam
Sara Zhalehpour
Andrew Piper
Chad Wellmon
Mohamed Cheriet
Copyright Year
2017
Book
Image Analysis and Recognition

Print ISBN: 978-3-319-59875-8

Electronic ISBN: 978-3-319-59876-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59876-5_71

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