NDLI: Farsi/Arabic handwritten digit recognition based on ensemble of SVD classifiers and reliable multi-phase PSO combination rule

Please wait, while we are loading the content...

International Journal of Document Analysis and Recognition (IJDAR) : Volume 20

International Journal of Document Analysis and Recognition (IJDAR) : Volume 19

International Journal of Document Analysis and Recognition (IJDAR) : Volume 18

International Journal of Document Analysis and Recognition (IJDAR) : Volume 17

International Journal of Document Analysis and Recognition (IJDAR) : Volume 16

International Journal of Document Analysis and Recognition (IJDAR) : Volume 16, Issue 4, December 2013

Image search in patents: a review

Detection of exact and similar partial copies for copyright protection of manga

Clutter noise removal in binary document images

Farsi/Arabic handwritten digit recognition based on ensemble of SVD classifiers and reliable multi-phase PSO combination rule

Visual word density-based nonlinear shape normalization method for handwritten Chinese character recognition

Effective technique for the recognition of offline Arabic handwritten words using hidden Markov models

Fast self-generation voting for handwritten Chinese character recognition

International Journal of Document Analysis and Recognition (IJDAR) : Volume 16, Issue 3, September 2013

International Journal of Document Analysis and Recognition (IJDAR) : Volume 16, Issue 2, June 2013

International Journal of Document Analysis and Recognition (IJDAR) : Volume 16, Issue 1, March 2013

International Journal of Document Analysis and Recognition (IJDAR) : Volume 15

International Journal of Document Analysis and Recognition (IJDAR) : Volume 14

International Journal of Document Analysis and Recognition (IJDAR) : Volume 13

International Journal of Document Analysis and Recognition (IJDAR) : Volume 12

International Journal of Document Analysis and Recognition (IJDAR) : Volume 11

International Journal of Document Analysis and Recognition (IJDAR) : Volume 10

International Journal of Document Analysis and Recognition (IJDAR) : Volume 9

International Journal of Document Analysis and Recognition (IJDAR) : Volume 8

International Journal of Document Analysis and Recognition (IJDAR) : Volume 7

International Journal of Document Analysis and Recognition (IJDAR) : Volume 6

International Journal of Document Analysis and Recognition (IJDAR) : Volume 5

International Journal of Document Analysis and Recognition (IJDAR) : Volume 4

International Journal of Document Analysis and Recognition (IJDAR) : Volume 3

International Journal of Document Analysis and Recognition (IJDAR) : Volume 2

International Journal of Document Analysis and Recognition (IJDAR) : Volume 1

Farsi/Arabic handwritten digit recognition based on ensemble of SVD classifiers and reliable multi-phase PSO combination rule

Content Provider	Springer Nature Link
Author	Salimi, Hamid Giveki, Davar
Copyright Year	2012
Abstract	The problem of handwritten digit recognition has long been an open problem in the field of pattern classification and of great importance in industry. The heart of the problem lies within the ability to design an efficient algorithm that can recognize digits written and submitted by users via a tablet, scanner, and other digital devices. From an engineering point of view, it is desirable to achieve a good performance within limited resources. To this end, we have developed a new approach for handwritten digit recognition that uses a small number of patterns for training phase. To improve the overall performance achieved in classification task, the literature suggests combining the decision of multiple classifiers rather than using the output of the best classifier in the ensemble; so, in this new approach, an ensemble of classifiers is used for the recognition of handwritten digit. The classifiers used in proposed system are based on singular value decomposition (SVD) algorithm. The experimental results and the literature show that the SVD algorithm is suitable for solving sparse matrices such as handwritten digit. The decisions obtained by SVD classifiers are combined by a novel proposed combination rule which we named reliable multi-phase particle swarm optimization. We call the method “Reliable” because we have introduced a novel reliability parameter which is applied to tackle the problem of PSO being trapped in local minima. In comparison with previous methods, one of the significant advantages of the proposed method is that it is not sensitive to the size of training set. Unlike other methods, the proposed method uses just 15 % of the dataset as a training set, while other methods usually use (60–75) % of the whole dataset as the training set. To evaluate the proposed method, we tested our algorithm on Farsi/Arabic handwritten digit dataset. What makes the recognition of the handwritten Farsi/Arabic digits more challenging is that some of the digits can be legally written in different shapes. Therefore, 6000 hard samples (600 samples per class) are chosen by K-nearest neighbor algorithm from the HODA dataset which is a standard Farsi/Arabic digit dataset. Experimental results have shown that the proposed method is fast, accurate, and robust against the local minima of PSO. Finally, the proposed method is compared with state of the art methods and some ensemble classifier based on MLP, RBF, and ANFIS with various combination rules.
Starting Page	371
Ending Page	386
Page Count	16
File Format	PDF
ISSN	14332833
Journal	International Journal of Document Analysis and Recognition (IJDAR)
Volume Number	16
Issue Number	4
e-ISSN	14332825
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2012-12-04
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Classifiers combination Two-dimensional PCA (2DPCA) Singular value decomposition (SVD) Particle swarm optimization (PSO) Reliability Image Processing and Computer Vision Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Handwritten Character Recognition in Ancient Manuscripts

Arabic handwritten digit recognition

A novel framework for Farsi and latin script identification and Farsi handwritten digit recognition

A watermarking scheme based on Singular Value Decomposition and Particle Swarm Optimization

Farsi/Arabic handwritten digit recognition using quantum neural networks and bag of visual words method

Toward automatic development of handwritten personal Farsi/Arabic OpenType $$^{\textregistered }$$ fonts

Performance Comparison of Various Particle Swarm Optimizers in DWT-SVD watermarking for RGB Images

Ensembles of classifiers for handwritten word recognition

Standard Databases for Recognition of Handwritten Digits, Numerical Strings, Legal Amounts, Letters and Dates in Farsi Language

Farsi/Arabic handwritten digit recognition based on ensemble of SVD classifiers and reliable multi-phase PSO combination rule

Similar Documents

Handwritten Character Recognition in Ancient Manuscripts

Arabic handwritten digit recognition

A novel framework for Farsi and latin script identification and Farsi handwritten digit recognition

A watermarking scheme based on Singular Value Decomposition and Particle Swarm Optimization

Farsi/Arabic handwritten digit recognition using quantum neural networks and bag of visual words method

Toward automatic development of handwritten personal Farsi/Arabic OpenType $$^{\textregistered }$$ fonts

Performance Comparison of Various Particle Swarm Optimizers in DWT-SVD watermarking for RGB Images

Ensembles of classifiers for handwritten word recognition

Standard Databases for Recognition of Handwritten Digits, Numerical Strings, Legal Amounts, Letters and Dates in Farsi Language

Farsi/Arabic handwritten digit recognition based on ensemble of SVD classifiers and reliable multi-phase PSO combination rule