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Genetic Algorithm Based Hybrid Attribute Selection Using Customized Fitness Function Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Combined Classifier Approach for Offline Handwritten Devanagari Character Recognition Using Multiple Features

verfasst von : Milind Bhalerao, Sanjiv Bonde, Abhijeet Nandedkar, Sushma Pilawan

Erschienen in: Computational Vision and Bio Inspired Computing

Verlag: Springer International Publishing

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Abstract

Offline handwritten character recognition is the process of recognizing given characters from the large set of characters. OCR system mainly focuses on the recognition of printed or handwritten characters of a scanned image. The proposed system extracts features that are based only on gradient of image which is helpful in exact recognition of characters. A technique to recognize handwritten Devanagari characters using combination of quadratic and SVM classifiers is presented in this paper. Features used are directional features that are strength, angle and histogram of gradient (SOG, AOG, HOG). Using a Gaussian filter, the strength and the angle features are down sampled to obtain a feature vector of 392 dimensions. These features are finally concatenated with HOG feature. Applying these to the combination of quadratic and SVM classifiers to obtain maximum accuracy of 95.81% using 3 fold cross validation.

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Vorheriges Kapitel An Autonomous Trust Model for Cloud Integrated Framework
Nächstes Kapitel Comprehensive Study on Usage of Multi Objectives in Recommender Systems
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Metadaten
Titel
Combined Classifier Approach for Offline Handwritten Devanagari Character Recognition Using Multiple Features
verfasst von
Milind Bhalerao
Sanjiv Bonde
Abhijeet Nandedkar
Sushma Pilawan
Copyright-Jahr
2018
Buch
Computational Vision and Bio Inspired Computing

Print ISBN: 978-3-319-71766-1

Electronic ISBN: 978-3-319-71767-8

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-71767-8_4

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