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Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 4/2015

01.11.2015 | Industrial and Commercial Application

A deep HMM model for multiple keywords spotting in handwritten documents

verfasst von: Simon Thomas, Clément Chatelain, Laurent Heutte, Thierry Paquet, Yousri Kessentini

Erschienen in: Pattern Analysis and Applications | Ausgabe 4/2015

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Abstract

In this paper, we propose a query by string word spotting system able to extract arbitrary keywords in handwritten documents, taking both segmentation and recognition decisions at the line level. The system relies on the combination of a HMM line model made of keyword and non-keyword (filler) models, with a deep neural network that estimates the state-dependent observation probabilities. Experiments are carried out on RIMES database, an unconstrained handwritten document database that is used for benchmarking different handwriting recognition tasks. The obtained results show the superiority of the proposed framework over the classical GMM–HMM and standard HMM hybrid architectures.
Vorheriger Artikel The k-NN classifier and self-adaptive Hotelling data reduction technique in handwritten signatures recognition

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Fußnoten
1
An extension of this approach has been proposed in [6], where the HMM is replaced by a combination of a LSTM neural network with a CTC algorithm.
 
2
Note that a Restricted Boltzman Machine (RBM) can also be used, in this case they are called Deep Belief Networks [33].
 
3
A vertical scaling procedure is applied to normalize the height of the text line image to \(h=54\).
 
4
An alternative to this strategy is to use a small \(\varepsilon\), with a maximum number of iterations, as it is the case in [45].
 
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Metadaten
Titel
A deep HMM model for multiple keywords spotting in handwritten documents
verfasst von
Simon Thomas
Clément Chatelain
Laurent Heutte
Thierry Paquet
Yousri Kessentini
Publikationsdatum
01.11.2015
Verlag
Springer London
Erschienen in
Pattern Analysis and Applications / Ausgabe 4/2015
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-014-0433-3

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