Open AccessPosted Content
Recognition of Non-Compound Handwritten Devnagari Characters using a Combination of MLP and Minimum Edit Distance
TLDR
A new method for recognition of offline Handwritten non-compound Devnagari Characters in two stages uses two well known and established pattern recognition techniques: one using neural networks and the other one using minimum edit distance.Abstract:
This paper deals with a new method for recognition of offline Handwritten non-compound Devnagari Characters in two stages. It uses two well known and established pattern recognition techniques: one using neural networks and the other one using minimum edit distance. Each of these techniques is applied on different sets of characters for recognition. In the first stage, two sets of features are computed and two classifiers are applied to get higher recognition accuracy. Two MLP's are used separately to recognize the characters. For one of the MLP's the characters are represented with their shadow features and for the other chain code histogram feature is used. The decision of both MLP's is combined using weighted majority scheme. Top three results produced by combined MLP's in the first stage are used to calculate the relative difference values. In the second stage, based on these relative differences character set is divided into two. First set consists of the characters with distinct shapes and second set consists of confused characters, which appear very similar in shapes. Characters of distinct shapes of first set are classified using MLP. Confused characters in second set are classified using minimum edit distance method. Method of minimum edit distance makes use of corner detected in a character image using modified Harris corner detection technique. Experiment on this method is carried out on a database of 7154 samples. The overall recognition is found to be 90.74%.read more
Citations
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Journal ArticleDOI
Offline Recognition of Devanagari Script: A Survey
TL;DR: In this paper, the state of the art from 1970s of machine printed and handwritten Devanagari optical character recognition (OCR) is discussed in various sections of the paper.
Posted Content
A Review of Research on Devnagari Character Recognition
Vikas J. Dongre,Vijay H. Mankar +1 more
TL;DR: An overview of DOCR systems is presented and the available DOCR techniques are reviewed in this article, where the current status of the DOCR is discussed and directions for future research are suggested.
Journal ArticleDOI
Handwritten Devanagari Character Recognition Using Layer-Wise Training of Deep Convolutional Neural Networks and Adaptive Gradient Methods
Mahesh Jangid,Sumit Srivastava +1 more
TL;DR: A layer-wise technique of DCNN has been employed that helped to achieve the highest recognition accuracy and also get a faster convergence rate and the results are favorable in comparison with those achieved by a shallow technique of handcrafted features and standard DCNN.
Journal ArticleDOI
A Review of Research on Devnagari Character Recognition
Vikas J. Dongre,Vijay H. Mankar +1 more
TL;DR: An overview of DOCR systems is presented and the available DOCR techniques are reviewed, and the current status ofDOCR is discussed and directions for future research are suggested.
Journal ArticleDOI
A Review on Feature Extraction and Feature Selection for Handwritten Character Recognition
TL;DR: An overview of some of the methods and approach of feature extraction and selection in handwriting character recognition, and the review of metaheuristic harmony search algorithm (HSA) has provide.
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