Share Email Print
cover

Proceedings Paper

Removing noise from handwritten character images using U-Net through online learning
Author(s): Rina Komatsu; Tad Gonsalves
Format Member Price Non-Member Price
PDF $17.00 $21.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Today, recognizing offline handwritten character images is still hard challenge. This is because there are the obstacles, ‘noise’ produced in scanning process. Noise makes handwritten character distorted, murky, and blurred. As a result, it become hard to read and recognize these images for human. In this study, we tried to get rid of various noises using CNN architecture named “U-Net” to analyze 607,200 sample images consisting of 3,036 Japanese characters. Finally, our results indicate that the “U-Net” has efficient ability to remove noise and enhance the parts of strokes even through there are a huge variety of handwritten styles which includes various noises.

Paper Details

Date Published: 29 October 2018
PDF: 5 pages
Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108360K (29 October 2018); doi: 10.1117/12.2514045
Show Author Affiliations
Rina Komatsu, Sophia Univ. (Japan)
Tad Gonsalves, Sophia Univ. (Japan)


Published in SPIE Proceedings Vol. 10836:
2018 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

© SPIE. Terms of Use
Back to Top