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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 7/2020

01.01.2019 | Original Article

A GA based hierarchical feature selection approach for handwritten word recognition

verfasst von: Samir Malakar, Manosij Ghosh, Showmik Bhowmik, Ram Sarkar, Mita Nasipuri

Erschienen in: Neural Computing and Applications | Ausgabe 7/2020

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Abstract

Feature selection plays a key role in reducing the dimensionality of a feature vector by discarding redundant and irrelevant ones. In this paper, a Genetic Algorithm-based hierarchical feature selection (HFS) model has been designed to optimize the local and global features extracted from each of the handwritten word images under consideration. In this context, two recently developed feature descriptors based on shape and texture of the word images have been taken into account. Experimentation is conducted on an in-house dataset of 12,000 handwritten word samples written in Bangla script. This database comprises names of 80 popular cities of West Bengal, a state of India. Proposed model not only reduces the feature dimension by nearly 28%, but also enhances the performance of the handwritten word recognition (HWR) technique by 1.28% over the recognition performance obtained with unreduced feature set. Moreover, the proposed HFS-based HWR system performs better in comparison with some recently developed methods on the present dataset.
Vorheriger Artikel A hybrid unsupervised approach toward EEG epileptic spikes detection
Nächster Artikel Classification of calcified regions in atherosclerotic lesions of the carotid artery in computed tomography angiography images

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Metadaten
Titel
A GA based hierarchical feature selection approach for handwritten word recognition
verfasst von
Samir Malakar
Manosij Ghosh
Showmik Bhowmik
Ram Sarkar
Mita Nasipuri
Publikationsdatum
01.01.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 7/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3937-8

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