NDLI: Unconstrained handwritten numeral recognition using majority voting classifier

Content Provider	IEEE Xplore Digital Library
Author	Kumar, R. Goyal, M.K. Ahmed, P. Kumar, A.
Copyright Year	2012
Description	Author affiliation: Department of Information Technology, Sharda University, Greater Noida, India (Kumar, R.; Goyal, M.K.) \|\| Department of Computer Science, Sharda University, Greater Noida, India (Ahmed, P.; Kumar, A.)
Abstract	Unconstrained offline handwritten numeral recognition is a challenging problem. It is very difficult to find high recognition results using a single classifier. This paper presents a simple profile, combined local & global features and majority voting scheme classifier for unconstrained handwritten numeral recognition. The simple profile feature is computed by using the left, right, top and bottom profile of an image. A feature vector of length 112 is formed by combining all the profiles. The local feature vector is extracted by applying Daubechies wavelet transform on the four images that were obtained by applying the Kirsch operator, and the global features that are obtained by applying the same Daubechies wavelet transform on the original image. A feature vector of length 80 is formed by combining the 64 local and 16 global features. The feature vectors are the intensity of a pixel in the third level approximation component of an image. In this experiment four neural network classifiers: Multilayer feed forward, Pattern recognition, Cascade forward, Function fitting neural network classifiers & two statistical classifiers: Linear discriminant analysis and KNN classifiers are used for classifying these features. A majority voting scheme has been performed with three neural network classifier and KNN classifier. The performance is tested on MNIST dataset. The network was trained on 60,000 and tested on 10,000 numeral samples of which 98.05 % test samples are correctly recognized.
Starting Page	284
Ending Page	289
File Size	1191816
Page Count	6
File Format	PDF
ISBN	9781467329224
e-ISBN	9781467329255
DOI	10.1109/PDGC.2012.6449833
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-06
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image recognition Resilient backpropagation Kirsch operator Nonhomogeneous media Neural network classifiers Handwriting recognition Neural networks Support vector machine classification Simple prrofile Silicon Scaled Conjugate Backpropagation Feeds Unconstrained handwritten numeral Majority voting
Content Type	Text
Resource Type	Article

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
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