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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 13/2020

28.08.2019 | Original Article

Hybrid HMM/BLSTM system for multi-script keyword spotting in printed and handwritten documents with identification stage

verfasst von: Ahmed Cheikhrouhou, Yousri Kessentini, Slim Kanoun

Erschienen in: Neural Computing and Applications | Ausgabe 13/2020

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Abstract

In this paper, we propose a novel script-independent approach for word spotting in printed and handwritten multi-script documents. Since each writing type and script need to be processed using a specific spotting engine, the proposed system proceeds on two stages. The script identification is a preliminary stage that aims at recognizing on one level the writing type and the script of the input image document. Second, a specific word spotting method is used to spot query words in a large collection of documents. The proposed spotting system is based on deep bidirectional long short-term memory neural network and hidden Markov model (HMM) hybrid architecture. It takes advantage of DNN’s strong representation learning power and HMM’s sequential modeling ability. The global system has been evaluated on a mixed corpus of public databases such as KHATT, PKHATT for Arabic script and RIMES for Latin script. The experimental results on script identification and keyword spotting confirm the effectiveness of the proposed approach.
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Fußnoten
1
The precision/recall break-even point is the value at which the precision is equal to the recall.
 
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Metadaten
Titel
Hybrid HMM/BLSTM system for multi-script keyword spotting in printed and handwritten documents with identification stage
verfasst von
Ahmed Cheikhrouhou
Yousri Kessentini
Slim Kanoun
Publikationsdatum
28.08.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 13/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04429-w

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