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Finger Motion In Classifying Offline Handwriting Patterns

Offline handwriting recognition refers to the ability of a machine to receive and interpret a previous individual-made handwritten input from a photographed or scanned image. In previous studies, the offline handwriting classification is determined solely based on the handwriting patterns. To the...

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Main Author: Yeoh, Shen Horng
Format: Monograph
Language:English
Published: Universiti Sains Malaysia 2017
Subjects:
T Technology
TJ Mechanical engineering and machinery
Online Access:http://eprints.usm.my/53706/1/Finger%20Motion%20In%20Classifying%20Offline%20Handwriting%20Patterns_%20Yeoh%20Shen%20Horng_M4_2017.pdf
http://eprints.usm.my/53706/
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spelling my.usm.eprints.53706 http://eprints.usm.my/53706/ Finger Motion In Classifying Offline Handwriting Patterns Yeoh, Shen Horng T Technology TJ Mechanical engineering and machinery Offline handwriting recognition refers to the ability of a machine to receive and interpret a previous individual-made handwritten input from a photographed or scanned image. In previous studies, the offline handwriting classification is determined solely based on the handwriting patterns. To the best of our knowledge, no studies were found to predict the English words inclination based on the finger motions. Therefore, this study aims to relate the finger movements to handwriting patterns. The specific objectives include: (i) to determine whether finger motion attributes can distinguish patterns of handwriting, (ii) classify handwriting patterns by sentence inclination based on different finger motion, (iii) to investigate the rule-reasoning statements between the finger motion and the handwriting inclinations. This study involves the features extractions from handwriting patterns of 30 subjects with recorded videos of finger movements during writings. Raw data undergo three stages of data mining analyses; data preprocessing, data classification and data interpretation. The preprocessed data is classified using the J48 tree algorithm. The correctly classified accuracy prediction after trained could achieve up to 98 %, Finding revealed that the angle of thumbs plays a significant role in classification of the inclination of the English sentence. Universiti Sains Malaysia 2017-05-01 Monograph NonPeerReviewed application/pdf en http://eprints.usm.my/53706/1/Finger%20Motion%20In%20Classifying%20Offline%20Handwriting%20Patterns_%20Yeoh%20Shen%20Horng_M4_2017.pdf Yeoh, Shen Horng (2017) Finger Motion In Classifying Offline Handwriting Patterns. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Mekanik. (Submitted)
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic T Technology
TJ Mechanical engineering and machinery
spellingShingle T Technology
TJ Mechanical engineering and machinery
Yeoh, Shen Horng
Finger Motion In Classifying Offline Handwriting Patterns
description Offline handwriting recognition refers to the ability of a machine to receive and interpret a previous individual-made handwritten input from a photographed or scanned image. In previous studies, the offline handwriting classification is determined solely based on the handwriting patterns. To the best of our knowledge, no studies were found to predict the English words inclination based on the finger motions. Therefore, this study aims to relate the finger movements to handwriting patterns. The specific objectives include: (i) to determine whether finger motion attributes can distinguish patterns of handwriting, (ii) classify handwriting patterns by sentence inclination based on different finger motion, (iii) to investigate the rule-reasoning statements between the finger motion and the handwriting inclinations. This study involves the features extractions from handwriting patterns of 30 subjects with recorded videos of finger movements during writings. Raw data undergo three stages of data mining analyses; data preprocessing, data classification and data interpretation. The preprocessed data is classified using the J48 tree algorithm. The correctly classified accuracy prediction after trained could achieve up to 98 %, Finding revealed that the angle of thumbs plays a significant role in classification of the inclination of the English sentence.
format Monograph
author Yeoh, Shen Horng
author_facet Yeoh, Shen Horng
author_sort Yeoh, Shen Horng
title Finger Motion In Classifying Offline Handwriting Patterns
title_short Finger Motion In Classifying Offline Handwriting Patterns
title_full Finger Motion In Classifying Offline Handwriting Patterns
title_fullStr Finger Motion In Classifying Offline Handwriting Patterns
title_full_unstemmed Finger Motion In Classifying Offline Handwriting Patterns
title_sort finger motion in classifying offline handwriting patterns
publisher Universiti Sains Malaysia
publishDate 2017
url http://eprints.usm.my/53706/1/Finger%20Motion%20In%20Classifying%20Offline%20Handwriting%20Patterns_%20Yeoh%20Shen%20Horng_M4_2017.pdf
http://eprints.usm.my/53706/
_version_ 1739829015749853184
score 13.211869

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