Optical character recognition in off-line handwritten recognition system to improve data encoding efficiency

Optical character recognition in off-line handwritten recognition system to improve data encoding efficiency

This research project aims to provide a faster and a more efficient encoding of daily time records (DTRs of employees. The encoding of DTRs is usually done manually by companies which takes a lot of work and time. So the group comes up with an alternative way to encode these DTRs. The group designed...

Full description

Saved in:
Bibliographic Details
Main Authors: Cabuenas, Marvin, Gacutan, Kristoffer, Lim, Catherine, Ngo, Stephen
Format: text
Language:English
Published: Animo Repository 2012
Online Access:https://animorepository.dlsu.edu.ph/etd_bachelors/14809
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: De La Salle University
Language: English
id oai:animorepository.dlsu.edu.ph:etd_bachelors-15451
record_format eprints
spelling oai:animorepository.dlsu.edu.ph:etd_bachelors-154512021-11-25T08:12:25Z Optical character recognition in off-line handwritten recognition system to improve data encoding efficiency Cabuenas, Marvin Gacutan, Kristoffer Lim, Catherine Ngo, Stephen This research project aims to provide a faster and a more efficient encoding of daily time records (DTRs of employees. The encoding of DTRs is usually done manually by companies which takes a lot of work and time. So the group comes up with an alternative way to encode these DTRs. The group designed an application that will translate the DTRs to an editable text format. This is done by using a scanner to convert the DTRs to image files afterwards, an Optical Character Recognition (OCR) software, to translate the image files to an editable text file. All the character translation processes are done automatically without proofreading. 2012-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_bachelors/14809 Bachelor's Theses English Animo Repository
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
language English
description This research project aims to provide a faster and a more efficient encoding of daily time records (DTRs of employees. The encoding of DTRs is usually done manually by companies which takes a lot of work and time. So the group comes up with an alternative way to encode these DTRs. The group designed an application that will translate the DTRs to an editable text format. This is done by using a scanner to convert the DTRs to image files afterwards, an Optical Character Recognition (OCR) software, to translate the image files to an editable text file. All the character translation processes are done automatically without proofreading.
format text
author Cabuenas, Marvin
Gacutan, Kristoffer
Lim, Catherine
Ngo, Stephen
spellingShingle Cabuenas, Marvin
Gacutan, Kristoffer
Lim, Catherine
Ngo, Stephen
Optical character recognition in off-line handwritten recognition system to improve data encoding efficiency
author_facet Cabuenas, Marvin
Gacutan, Kristoffer
Lim, Catherine
Ngo, Stephen
author_sort Cabuenas, Marvin
title Optical character recognition in off-line handwritten recognition system to improve data encoding efficiency
title_short Optical character recognition in off-line handwritten recognition system to improve data encoding efficiency
title_full Optical character recognition in off-line handwritten recognition system to improve data encoding efficiency
title_fullStr Optical character recognition in off-line handwritten recognition system to improve data encoding efficiency
title_full_unstemmed Optical character recognition in off-line handwritten recognition system to improve data encoding efficiency
title_sort optical character recognition in off-line handwritten recognition system to improve data encoding efficiency
publisher Animo Repository
publishDate 2012
url https://animorepository.dlsu.edu.ph/etd_bachelors/14809
_version_ 1772835068087107584

Similar Items