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Chantana Chantrapornchai. Hand writing recognition model enhancement exploration based on skipped connections. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2020.
Hand writing recognition model enhancement exploration based on skipped connections
Name: Phattharaphon Romphet
Organization :
Kasetsart University. Faculty of Engineering
Email :
phattharaphon.r@ku.th
Name: Supasit Kajkamhaeng
Organization :
Kasetsart University. Faculty of Engineering
Email :
supasit.k@ku.th
Name: Chantana Chantrapornchai
Organization :
Kasetsart University. Faculty of Engineering
Email :
fengcnc@ku.ac.th
keyword:
Skipped Connection
ThaSH:
Character recognition
ThaSH:
Models and modelmaking
ThaSH:
Artificial intelligence
Abstract:
Handwriting recognition is a well-known problem
utilizing a deep learning approach. Due to the variety of words
and writing styles, a large data set is required to build the
good model. In this research, we investigate the state-of-theart
handwriting model, namely Flor model [1], with our data
set, and explore the extensions to improve the accuracy. The
improvement aims to use skipped connections and learn to
pass through features properly. The possible skipped connection
insertion approaches are studied. We also consider the model
size along with the accuracy improvement. We use the modified
data set originally from Research Group on Computer Vision
and Artificial Intelligence INF, University of Bern [2], containing
112,7446 words. The results show that our refinement can lead
to the reduction of 1.5 percent in character base error and 2 percent in
word base error with the unseen test data set.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2020
Modified:
2023-11-16
Issued:
2023-11-16
บทความ/Article
application/pdf
BibliograpyCitation :
In King Mongkut's University of Technology North Bangkok Faculty of Applied Science. The 17th International Joint Conference on Computer Science and Software Engineering (JCSSE 2020) (pp.122-127). Bangkok : King Mongkut's University of Technology North Bangkok
eng
©copyrights King Mongkut's University of Technology North Bangkok
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0.026916 วินาที
Phattharaphon Romphet
| Title | Contributor | Type |
|---|---|---|
| Hand writing recognition model enhancement exploration based on skipped connections
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Phattharaphon Romphet;Supasit Kajkamhaeng;Chantana Chantrapornchai | บทความ/Article |
Supasit Kajkamhaeng
| Title | Contributor | Type |
|---|---|---|
| Hand writing recognition model enhancement exploration based on skipped connections
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Phattharaphon Romphet;Supasit Kajkamhaeng;Chantana Chantrapornchai | บทความ/Article |
Chantana Chantrapornchai
| Title | Contributor | Type |
|---|---|---|
| Hand writing recognition model enhancement exploration based on skipped connections
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Phattharaphon Romphet;Supasit Kajkamhaeng;Chantana Chantrapornchai | บทความ/Article | |
| Comparison study of 3D object matching techniques for UV projection mapping image feature-based and neural network-based approaches
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Narawich Maitreejitr.;Chantana Chantrapornchai. | บทความ/Article |