Combining Multiple Feature Extraction Techniques for Handwritten Devnagari Character Recognition

Arora, Saurabh; Bhattacharjee, Debotosh; Nasipuri, Mita; Basu, Dipak Kumar; Kundu, Mahantapas

doi:10.1109/iciinfs.2008.4798415

Cited by 64 publications

(28 citation statements)

References 12 publications

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“…They construct a 124-variable feature vector comprising following seven families of features: 1) intersection of the character with horizontal and vertical straight lines, 2) invariant moments, 3) holes and concave arcs, 4) extremas, 5) end points and junction points 6) profiles, and 7) projections. Aurora et al [53] combine different feature extraction techniques such as intersection based features, shadow features, chain code and curve fitting features for Indian Devnagari language script. Kimura et al [54] propose a genetic algorithm based strategy for finding a suitable combination of features from a large pool of features with the objective criteria to minimize the classification error.…”

Section: Current Trends In Feature Extractionmentioning

confidence: 99%

A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition

Mohamad¹,

Hassan²,

Nasien³

et al. 2015

ijacsa

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Abstract-The development of handwriting character recognition (HCR) is an interesting area in pattern recognition.HCR system consists of a number of stages which are preprocessing, feature extraction, classification and followed by the actual recognition. It is generally agreed that one of the main factors influencing performance in HCR is the selection of an appropriate set of features for representing input samples. This paper provides a review of these advances. In a HCR, the set of features plays as main issues, as procedure in choosing the relevant feature that yields minimum classification error. To overcome these issues and maximize classification performance, many techniques have been proposed for reducing the dimensionality of the feature space in which data have to be processed. These techniques, generally denoted as feature reduction, may be divided in two main categories, called feature extraction and feature selection. A large number of research papers and reports have already been published on this topic. In this paper we provide an overview of some of the methods and approach of feature extraction and selection. Throughout this paper, we apply the investigation and analyzation of feature extraction and selection approaches in order to obtain the current trend. Throughout this paper also, the review of metaheuristic harmony search algorithm (HSA) has provide.

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Section: Current Trends In Feature Extractionmentioning

confidence: 99%

A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition

Mohamad¹,

Hassan²,

Nasien³

et al. 2015

ijacsa

View full text Add to dashboard Cite

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“…Feature extraction is extracting information from raw data which is most [7] relevant for classification purpose. In feature extraction stage every character is assigned a feature vector to identify it.…”

Section: Feature Extractionmentioning

confidence: 99%

Design and Simulation of Handwritten Gurumukhi and Devanagri Numerals Recognition

Anjum¹,

Bali²,

Raj³

2013

IJCA

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The work presented in this paper focuses on recognition of isolated handwritten numerals in Devanagari and Gurumukhi script. The proposed work uses four feature extraction methods like Zoning density, Projection histograms, Distance profiles and Background Directional Distribution(BDD). On the basis of these four types of features we have formed 10 feature vectors using different combinations of four basic features. This work uses Support Vector machines(SVM) for the classification of numerals. A total of 2000 samples of numerals are taken for Gurumukhi and Devanagari and we have attain a maximum recognition accuracy of 99.6% in case of Gurumukhi Numeral recognition and 99% for Devanagri Numeral recognition. In addition to SVM classifier , we have also used two similarity based classifiers Euclidean distance and Square chord distance for the classification purpose. With Euclidean distance ,a recognition accuracy of 99% and 91.67% is obtained for Gurumukhi and Devanagri numarals respectively. Similarly with Square Chord distance accuracy of 95.33% and 81.67% is obtained for Gurumukhi and devanagri numerals respectively

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“…In world Devanagari script is third most widely used script, used for several major languages such as Hindi, Sanskrit, Marathi and Nepali, and is used by more than 500 million people [1]. From different scripts unconstrained Devanagari writing is more complex than English cursive due to the possible variations in the order, number, direction and shape of the constituent strokes.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Off-Line Handwritten Devanagari Characters using Combinational Feature Extraction

Birajdar¹,

Patil²

2015

IJCA

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Handwritten Devanagari off-line characters recognition is a challenging task due to peculiarities involved in the writing styles of different person. We are collected features from 64 dimensional feature extraction techniques and shadow features for the Devanagari character recognition in this proposed scheme. These calculated features are used for further classification. Features which are used here for recognition means histograms of direction chain code of the contour points of the characters [4]. The features are classified using neural network and selected the best result by weighted majority voting technique. General TermsThe general terms are Neural Network classifier is used with MLP classifier. Keywords64 dimensional features, Shadow features, neural network and weighted majority voting technique.

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Combining Multiple Feature Extraction Techniques for Handwritten Devnagari Character Recognition

Cited by 64 publications

References 12 publications

A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition

A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition

Design and Simulation of Handwritten Gurumukhi and Devanagri Numerals Recognition

Recognition of Off-Line Handwritten Devanagari Characters using Combinational Feature Extraction

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