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International Journal on Document Analysis and Recognition (IJDAR)
Erschienen in: International Journal on Document Analysis and Recognition (IJDAR) 3/2018

14.02.2018 | Special Issue Paper

Attribute CNNs for word spotting in handwritten documents

verfasst von: Sebastian Sudholt, Gernot A. Fink

Erschienen in: International Journal on Document Analysis and Recognition (IJDAR) | Ausgabe 3/2018

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Abstract

Word spotting has become a field of strong research interest in document image analysis over the last years. Recently, AttributeSVMs were proposed which predict a binary attribute representation (Almazán et al. in IEEE Trans Pattern Anal Mach Intell 36(12):2552–2566, 2014). At their time, this influential method defined the state of the art in segmentation-based word spotting. In this work, we present an approach for learning attribute representations with convolutional neural networks(CNNs). By taking a probabilistic perspective on training CNNs, we derive two different loss functions for binary and real-valued word string embeddings. In addition, we propose two different CNN architectures, specifically designed for word spotting. These architectures are able to be trained in an end-to-end fashion. In a number of experiments, we investigate the influence of different word string embeddings and optimization strategies. We show our attribute CNNs to achieve state-of-the-art results for segmentation-based word spotting on a large variety of data sets.
Vorheriger Artikel Integrating scattering feature maps with convolutional neural networks for Malayalam handwritten character recognition
Nächster Artikel Fixed-sized representation learning from offline handwritten signatures of different sizes

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Fußnoten
7
We denote the classic stochastic gradient descent optimization as SGD and the Adam optimization [21] as Adam although technically Adam is a form of stochastic gradient descent as well.
 
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Metadaten
Titel
Attribute CNNs for word spotting in handwritten documents
verfasst von
Sebastian Sudholt
Gernot A. Fink
Publikationsdatum
14.02.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal on Document Analysis and Recognition (IJDAR) / Ausgabe 3/2018
Print ISSN: 1433-2833
Elektronische ISSN: 1433-2825
DOI
https://doi.org/10.1007/s10032-018-0295-0

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