NDLI: Gaussian mixture model based handwritten numeral character recognition

Content Provider	IEEE Xplore Digital Library
Author	Usman Akram, M. Tariq, A. Bashir, Z. Khan, S.A.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Eng., Nat. Univ. of Sci. & Technol., Pakistan (Usman Akram, M.; Tariq, A.; Bashir, Z.; Khan, S.A.)
Abstract	Automated character recognition is a wide field and current area of research in image processing and pattern recognition. It has its applications in optical character recognition, handwritten character recognition, postal code readers, car number plate identification and even in biometrics for identification of persons on basis of their handwritings. In this paper, we present an automated system for identification and classification of handwritten numeral characters. Our system consists of three stages i.e. preprocessing, feature extraction and classification. We propose intensity, shape and geometric based features for accurate representation of each numeral character. The system applies a Gaussian Mixture Model using expectation maximization for classification of input characters. In order to check the accuracy of proposed system, we use United States Postal Service (USPS) database and the results show the validity of proposed system.
Sponsorship	IEEE Malaysia Sect. IE/IA Joint Chapter
Starting Page	81
Ending Page	85
File Size	642603
Page Count	5
File Format	PDF
e-ISBN	9781479911257
DOI	10.1109/ISIEA.2013.6738972
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-22
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Handwriting recognition Accuracy Databases Feature extraction Character recognition
Content Type	Text
Resource Type	Article

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