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HMM based Offline Handwritten Writer Independent English Character Recognition using Global and Local Feature Extraction

by Rajib Lochan Das, Binod Kumar Prasad, Goutam Sanyal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 46 - Number 10
Year of Publication: 2012
Authors: Rajib Lochan Das, Binod Kumar Prasad, Goutam Sanyal
10.5120/6948-9428

Rajib Lochan Das, Binod Kumar Prasad, Goutam Sanyal . HMM based Offline Handwritten Writer Independent English Character Recognition using Global and Local Feature Extraction. International Journal of Computer Applications. 46, 10 ( May 2012), 45-50. DOI=10.5120/6948-9428

@article{ 10.5120/6948-9428,
author = { Rajib Lochan Das, Binod Kumar Prasad, Goutam Sanyal },
title = { HMM based Offline Handwritten Writer Independent English Character Recognition using Global and Local Feature Extraction },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 46 },
number = { 10 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 45-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume46/number10/6948-9428/ },
doi = { 10.5120/6948-9428 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:39:45.635502+05:30
%A Rajib Lochan Das
%A Binod Kumar Prasad
%A Goutam Sanyal
%T HMM based Offline Handwritten Writer Independent English Character Recognition using Global and Local Feature Extraction
%J International Journal of Computer Applications
%@ 0975-8887
%V 46
%N 10
%P 45-50
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recognition rate of handwritten character is still limited around 90 percent due to the presence of large variation of shape, scale and format in hand written characters. A sophisticated hand written character recognition system demands a better feature extraction technique that would take care of such variation of hand writing. In this paper, we propose a recognition model based on multiple Hidden Markov Models (HMMs) followed by few novel feature extraction techniques for a single character to tackle its different writing formats. We also propose a post-processing block at the final stage to enhance the recognition rate further. We have created a data-base of 13000 samples collected from 100 writers written five times for each character. 2600 samples have been used to train HMM and the rest are used to test recognition model. Using our proposed recognition system we have achieved a good average recognition rate of 98. 26 percent.

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Index Terms

Computer Science
Information Sciences

Keywords

Hidden Markov Model Sobel Masks Gradient Features Curvature Features And Projected Histogram

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