Off-line handwritten character recognition using Hidden Markov Model

Author

Gayathri, P. ; Ayyappan, Sonal

Author_Institution

Dept. of Comput. Sci. & Eng., SCMS Sch. of Eng. & Technol., Ernakulam, India

fYear

2014

fDate

24-27 Sept. 2014

Firstpage

518

Lastpage

523

Abstract

In this paper, we are presenting a method for the recognition of Malayalam handwritten vowels using Hidden Markov Model (HMM). OCR is a method to detect characters in different sources. The goal of OCR is to classify optical patterns in an image to the corresponding characters. Recognition of handwritten Malayalam vowels is proposed in this paper. Images of the characters written by eighteen subjects are used for this experiment. Training and recognition are performed using Hidden Markov Model Toolkit. Recognition process involves several steps including image acquisition, dataset preparation, pre-processing, feature extraction, training and recognition. An average accuracy of about 81.38% has been obtained.

Keywords

feature extraction; handwritten character recognition; hidden Markov models; image classification; HMM; Malayalam handwritten vowel recognition; dataset preparation; feature extraction; hidden Markov model; image acquisition; offline handwritten character recognition; optical pattern classification; Character recognition; Feature extraction; Hidden Markov models; Image recognition; Markov processes; Optical character recognition software; Prototypes; Binarization; Feature Extraction; Handwritten character recognition; Hidden Markov Model; Normalization; Optical Character Recognition; Pre-processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on

Conference_Location

New Delhi

Print_ISBN

978-1-4799-3078-4

Type

conf

DOI

10.1109/ICACCI.2014.6968488

Filename

6968488