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Reseach Article

Handwritten Manuscript Digitizer

by Kaushil Ruparelia, Ashay Shah, Seema Wadhwani, M. Mani Roja
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 136 - Number 6
Year of Publication: 2016
Authors: Kaushil Ruparelia, Ashay Shah, Seema Wadhwani, M. Mani Roja
10.5120/ijca2016908467

Kaushil Ruparelia, Ashay Shah, Seema Wadhwani, M. Mani Roja . Handwritten Manuscript Digitizer. International Journal of Computer Applications. 136, 6 ( February 2016), 24-27. DOI=10.5120/ijca2016908467

@article{ 10.5120/ijca2016908467,
author = { Kaushil Ruparelia, Ashay Shah, Seema Wadhwani, M. Mani Roja },
title = { Handwritten Manuscript Digitizer },
journal = { International Journal of Computer Applications },
issue_date = { February 2016 },
volume = { 136 },
number = { 6 },
month = { February },
year = { 2016 },
issn = { 0975-8887 },
pages = { 24-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume136/number6/24158-2016908467/ },
doi = { 10.5120/ijca2016908467 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:36:20.670797+05:30
%A Kaushil Ruparelia
%A Ashay Shah
%A Seema Wadhwani
%A M. Mani Roja
%T Handwritten Manuscript Digitizer
%J International Journal of Computer Applications
%@ 0975-8887
%V 136
%N 6
%P 24-27
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In India, there are various instances where information is gathered by filling a questionnaire or a form. This information is then updated manually into the databases by the concerned authorities. Due to manual data entry, human error results in the capture of inaccurate data and thereby results in faulty storage and analysis of the data. The process is time consuming with a greater probability of error. This document serves as a guideline to automate and expedite the above process. The paper contains ideas of converting the handwritten samples into electronic data. It uses the kernel method of Multi class Support Vector Machine for handwritten character recognition. The data is first extracted in form of individual images for the corresponding data field, pre processed and converted to digital format. This reduces the time and human effort needed for the same. This paper aims at easing the process of evaluation by automating the correction process.

References
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Index Terms

Computer Science
Information Sciences

Keywords

HCR OCR Support Vector Machine Kernel trick.