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Open AccessJournal Article

Handwritten Devanagari Character Recognition Model Using Neural Network

nbspGaurav Jaiswal
- 01 Mar 2014 - 
- Vol. 2, Iss: 1, pp 901-906
TLDR
A recognition model is described for recognizing handwritten Devanagari characters and achieves the accuracy rate of recognition which range from 75% to 80%.
Abstract
In this paper, a recognition model is described for recognizing handwritten Devanagari characters. The scanned image database of handwritten Devanagari character form several different writers was used to train and test to this classifier model. This model first preprocess (normalization, binarization, crop) then extracts the feature set. Based on the extracted feature database it classifies the characters. This model achieves the accuracy rate of recognition which range from 75% to 80%.

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Citations
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Proceedings ArticleDOI

A Deep Learning Approach for Optical Character Recognition of Handwritten Devanagari Script

TL;DR: Development of Convolutional Neural Network (CNN) based Optical Character Recognition system (OCR) for Handwritten Devanagari Script which is observed to recognize the characters accurately.
Journal ArticleDOI

A Review of Different Approaches Used for Devanagari Character Recognition

TL;DR: Some of the popular research performed in recognizing Devanagari script are enlightened and various advantage and scope of using different methodology including Bounding Box technique, Ostu’s algorithm, neural networks and many more are summarized.
References
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Journal ArticleDOI

Statistical pattern recognition: a review

TL;DR: The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
Journal ArticleDOI

Review of shape representation and description techniques

TL;DR: This paper identifies some promising techniques for image retrieval according to standard principles and examines implementation procedures for each technique and discusses its advantages and disadvantages.
Journal ArticleDOI

Neural networks for classification: a survey

TL;DR: The issues of posterior probability estimation, the link between neural and conventional classifiers, learning and generalization tradeoff in classification, the feature variable selection, as well as the effect of misclassification costs are examined.
Journal ArticleDOI

Feature extraction methods for character recognition--a survey

TL;DR: This paper presents an overview of feature extraction methods for off-line recognition of segmented (isolated) characters in terms of invariance properties, reconstructability and expected distortions and variability of the characters.