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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/5653
Title: Feature set evaluation for offline handwriting recognition systems: Application to the recurrent neural network model
Authors: Chherawala Y.
Pratim Roy, Partha
Cheriet M.
Published in: IEEE Transactions on Cybernetics
Abstract: The performance of handwriting recognition systems is dependent on the features extracted from the word image. A large body of features exists in the literature, but no method has yet been proposed to identify the most promising of these, other than a straightforward comparison based on the recognition rate. In this paper, we propose a framework for feature set evaluation based on a collaborative setting. We use a weighted vote combination of recurrent neural network (RNN) classifiers, each trained with a particular feature set. This combination is modeled in a probabilistic framework as a mixture model and two methods for weight estimation are described. The main contribution of this paper is to quantify the importance of feature sets through the combination weights, which reflect their strength and complementarity. We chose the RNN classifier because of its state-of-the-art performance. Also, we provide the first feature set benchmark for this classifier. We evaluated several feature sets on the IFN/ENIT and RIMES databases of Arabic and Latin script, respectively. The resulting combination model is competitive with state-of-the-art systems. © 2015 IEEE.
Citation: IEEE Transactions on Cybernetics (2015), 46(10): -
URI: https://doi.org/10.1109/TCYB.2015.2490165
http://repository.iitr.ac.in/handle/123456789/5653
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers Inc.
Keywords: Feature set evaluation
Institut fur nachrichtentechnik/ecole nationale d'ingenieurs de Tunis (IFN/ENIT)
Reconnaissance et indexation de donnees manuscrites et de fac similes (RIMES)
Recurrent neural network (RNN)
System combination
Word recognition
ISSN: 21682267
Author Scopus IDs: 53864607500
56880478500
56216876600
Author Affiliations: Chherawala, Y., Synchromedia Laboratory for Multimedia Communication in Telepresence, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada
Roy, P.P., Synchromedia Laboratory for Multimedia Communication in Telepresence, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada
Cheriet, M., Synchromedia Laboratory for Multimedia Communication in Telepresence, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada
Appears in Collections:Journal Publications [CS]

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