Study of Different Features on Handwritten Devnagari Character

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

doi:10.1109/icetet.2009.215

Cited by 26 publications

(11 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several feature Extraction methods are available for Devnagari characters and digits recognition but the time required and accuracy is not optimized. Golait, Snehal S., and Latesh G. Malik [27] suggested faster efficient and optimized feature extraction method for character and digits recognition. In a further subsection, the features extraction and the classification is clarified in detail.…”

Section: Proposed Methodologymentioning

confidence: 99%

Conversion and Recognition of Handwritten Devnagari Character String into Printed Character String Using KNN

Pilawan¹,

Bhalerao²,

Nandedkar³

et al. 2017

IJERT

View full text Add to dashboard Cite

Abstract-This paper presents a system for the conversion of handwritten string of Devnagari character to printed character string by using character segmentation approach. 11 different statistical features of segmented characters are extracted which are compared with features extracted from printed string of characters available in training data for cross validation purpose using K-nearest neighborhood (kNN) algorithm.Use of handwritten string of Devnagari characters written in different styles and converting it into printed string makes the system more prone to real life application. System mainly works on segmentation of characters using bounding box, after segmentation, features are extracted which is compared with training feature set. We have analyzed our system with existing Devnagari handwritten character recognition systems. In given framework, we have focused on a creating database in different styles and recognizing them as printed characters.

show abstract

Section: Proposed Methodologymentioning

confidence: 99%

Conversion and Recognition of Handwritten Devnagari Character String into Printed Character String Using KNN

Pilawan¹,

Bhalerao²,

Nandedkar³

et al. 2017

IJERT

View full text Add to dashboard Cite

show abstract

“…This approach has been tested on 50,000 samples and obtained 89.12% accuracy. In [21], S. Arora combined different features such as chain codes, four side views, and shadow based features. These features were fed into a multilayer perceptron neural network to recognize 1500 handwritten Devanagari characters and obtain 89.58% accuracy.…”

Section: Previous Workmentioning

confidence: 99%

Handwritten Devanagari Character Recognition Using Layer-Wise Training of Deep Convolutional Neural Networks and Adaptive Gradient Methods

Jangid

Srivastava

2018

J. Imaging

View full text Add to dashboard Cite

Abstract:Handwritten character recognition is currently getting the attention of researchers because of possible applications in assisting technology for blind and visually impaired users, human-robot interaction, automatic data entry for business documents, etc. In this work, we propose a technique to recognize handwritten Devanagari characters using deep convolutional neural networks (DCNN) which are one of the recent techniques adopted from the deep learning community. We experimented the ISIDCHAR database provided by (Information Sharing Index) ISI, Kolkata and V2DMDCHAR database with six different architectures of DCNN to evaluate the performance and also investigate the use of six recently developed adaptive gradient methods. A layer-wise technique of DCNN has been employed that helped to achieve the highest recognition accuracy and also get a faster convergence rate. The results of layer-wise-trained DCNN are favorable in comparison with those achieved by a shallow technique of handcrafted features and standard DCNN.

show abstract

“…The view is a set of points that plot one of four projections of the object (top, bottom, left and right) -it consists of pixels belonging to the contour of the character and having extreme values of one of its coordinates [3].…”

Section: View Based Featuresmentioning

confidence: 99%

“…If any of the 4-connected neighbor points is a background point then the background point, is considered as contour point [3].…”

Section: Chain Code Histogram Of Character Contourmentioning

confidence: 99%

Survey and Analysis of Devnagari Character Recognition Techniques using Neural Networks

Sahu¹,

Rathy²,

Kashyap³

2012

IJCA

View full text Add to dashboard Cite

English Character Recognition techniques have been studied extensively in the last few years and its progress and success rate is quite high. But for regional languages these are still emerging and their success rate is moderate. There are millions of people who speak Hindi and use Devnagari script for writing. As digital documentation in Devnagari script is gaining popularity. Research in Optical Character Recognition (OCR) is very essential especially with an eye on its applications in banks, post offices, defense organizations, library automation, etc. Devnagari Optical Character Recognition needs more attention as it is national language and there is less development in this field due to complexity in the script.This paper describes the current techniques being used for DOCR. The overview of the system is explained with the available techniques and their current status.

show abstract

Study of Different Features on Handwritten Devnagari Character

Cited by 26 publications

References 8 publications

Conversion and Recognition of Handwritten Devnagari Character String into Printed Character String Using KNN

Conversion and Recognition of Handwritten Devnagari Character String into Printed Character String Using KNN

Handwritten Devanagari Character Recognition Using Layer-Wise Training of Deep Convolutional Neural Networks and Adaptive Gradient Methods

Survey and Analysis of Devnagari Character Recognition Techniques using Neural Networks

Contact Info

Product

Resources

About