Handprint to text.

The thesis entitled, Handprint to Text , is basically a software enabling the user to convert a scanned handprinted text document into a text file. This text file may then be opened in word processors for printing or further refining. The acceptable scanned document should be a BMP file and the hand...

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Main Authors: Ching, John Paul, Chua, Ching, Lee, Bryan, Lee, Walter, Lim, John John
Format: text
Language:English
Published: Animo Repository 2000
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Online Access:https://animorepository.dlsu.edu.ph/etd_bachelors/10408
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Institution: De La Salle University
Language: English
id oai:animorepository.dlsu.edu.ph:etd_bachelors-11053
record_format eprints
spelling oai:animorepository.dlsu.edu.ph:etd_bachelors-110532021-09-16T08:16:08Z Handprint to text. Ching, John Paul Chua, Ching Lee, Bryan Lee, Walter Lim, John John The thesis entitled, Handprint to Text , is basically a software enabling the user to convert a scanned handprinted text document into a text file. This text file may then be opened in word processors for printing or further refining. The acceptable scanned document should be a BMP file and the handprint recognizing engine is the neural network, Bidirectional Associative Memories (BAM) developed by Bart Kosko. By doing so, the researcher will not have to worry about typing his work anymore. He would just have to scan his research draft, process the scanned work and open the text file in MS-Word and he instantly has his work typewritten. That way, the researcher has more time doing more important things other than typing. The neural network model was implemented in Turbo C and was trained to recognize all 26 uppercase letters. These letters were each represented as a 24 X 18 matrix consisting of either a value of 1 or -1. The matrix was then passed to the neural network for proper classification. A single weight matrix representing all the letters was not sufficient, therefore multiple weight matrices were used, one per letter. To further improve recognition, five sets of letters were used to train the neural network. That way, the neural network has more exemplar pairs to base its decision. The output of the neural network is a vector consisting of 8 bits. These bits were used to properly encode the text file. Prior to feature extraction, median filtering was employed to reduce noise in the image. After filtering, thresholding was done to set the image into two level colors only for ease of feature extraction. 2000-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_bachelors/10408 Bachelor's Theses English Animo Repository Optical character recognition--Computer programs Text processing (Computer science) Systems and Communications
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
topic Optical character recognition--Computer programs
Text processing (Computer science)
Systems and Communications
spellingShingle Optical character recognition--Computer programs
Text processing (Computer science)
Systems and Communications
Ching, John Paul
Chua, Ching
Lee, Bryan
Lee, Walter
Lim, John John
Handprint to text.
description The thesis entitled, Handprint to Text , is basically a software enabling the user to convert a scanned handprinted text document into a text file. This text file may then be opened in word processors for printing or further refining. The acceptable scanned document should be a BMP file and the handprint recognizing engine is the neural network, Bidirectional Associative Memories (BAM) developed by Bart Kosko. By doing so, the researcher will not have to worry about typing his work anymore. He would just have to scan his research draft, process the scanned work and open the text file in MS-Word and he instantly has his work typewritten. That way, the researcher has more time doing more important things other than typing. The neural network model was implemented in Turbo C and was trained to recognize all 26 uppercase letters. These letters were each represented as a 24 X 18 matrix consisting of either a value of 1 or -1. The matrix was then passed to the neural network for proper classification. A single weight matrix representing all the letters was not sufficient, therefore multiple weight matrices were used, one per letter. To further improve recognition, five sets of letters were used to train the neural network. That way, the neural network has more exemplar pairs to base its decision. The output of the neural network is a vector consisting of 8 bits. These bits were used to properly encode the text file. Prior to feature extraction, median filtering was employed to reduce noise in the image. After filtering, thresholding was done to set the image into two level colors only for ease of feature extraction.
format text
author Ching, John Paul
Chua, Ching
Lee, Bryan
Lee, Walter
Lim, John John
author_facet Ching, John Paul
Chua, Ching
Lee, Bryan
Lee, Walter
Lim, John John
author_sort Ching, John Paul
title Handprint to text.
title_short Handprint to text.
title_full Handprint to text.
title_fullStr Handprint to text.
title_full_unstemmed Handprint to text.
title_sort handprint to text.
publisher Animo Repository
publishDate 2000
url https://animorepository.dlsu.edu.ph/etd_bachelors/10408
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