An On-Line/Off-Line Compatible Character Recognition Method Based on a Dynamic Model
Summary :
This paper presents a character recognition method based on a dynamic model, which can be applied to character patterns from both on-line and off-line input. Other similar attempts simply treat on-line patterns as off-line input, while this method makes use of the on-line input's characteristics by representing the time information of handwriting in the character pattern representations. Experiments were carried out on the Hiragana character set. Without non-linear normalization, this method achieved recognition rates of 92.3% for on-line input and 89.1% for off-line input. When non-linear normalization is used, there is an increase in performance for both types of input with on-line input achieving 94.5% and off-line input achieving 94.1%. The reason for the difference in the effectiveness of non-linear normalization on off-line and on-line patterns could be that while the method used for off-line input was an established and proved one, we used our own initial attempt at non-linear normalization for the on-line patterns. If the same level of effectiveness of non-linear normalization as off-line input is achieved on the on-line input, however, the recognition rate for on-line input again improves becoming 96.3%. Since only one standard pattern was used per category for the dictionary patterns, the above results show the promise of this method. This result shows the compatibility of this method to both on-line and off-line input, as well as its effective use of on-line input's characteristics. The effectiveness of this use of the time information is shown by using an actual example. The data also shows the need for a method of non-linear normalization which is more suitable for on-line input.
- Publication
- IEICE TRANSACTIONS on Information Vol.E80-D No.6 pp.672-683
- Publication Date
- 1997/06/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Category
- Image Processing,Computer Graphics and Pattern Recognition
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Rodney WEBSTER, Masaki NAKAGAWA, "An On-Line/Off-Line Compatible Character Recognition Method Based on a Dynamic Model" in IEICE TRANSACTIONS on Information,
vol. E80-D, no. 6, pp. 672-683, June 1997, doi: .
Abstract: This paper presents a character recognition method based on a dynamic model, which can be applied to character patterns from both on-line and off-line input. Other similar attempts simply treat on-line patterns as off-line input, while this method makes use of the on-line input's characteristics by representing the time information of handwriting in the character pattern representations. Experiments were carried out on the Hiragana character set. Without non-linear normalization, this method achieved recognition rates of 92.3% for on-line input and 89.1% for off-line input. When non-linear normalization is used, there is an increase in performance for both types of input with on-line input achieving 94.5% and off-line input achieving 94.1%. The reason for the difference in the effectiveness of non-linear normalization on off-line and on-line patterns could be that while the method used for off-line input was an established and proved one, we used our own initial attempt at non-linear normalization for the on-line patterns. If the same level of effectiveness of non-linear normalization as off-line input is achieved on the on-line input, however, the recognition rate for on-line input again improves becoming 96.3%. Since only one standard pattern was used per category for the dictionary patterns, the above results show the promise of this method. This result shows the compatibility of this method to both on-line and off-line input, as well as its effective use of on-line input's characteristics. The effectiveness of this use of the time information is shown by using an actual example. The data also shows the need for a method of non-linear normalization which is more suitable for on-line input.
URL: https://global.ieice.org/en_transactions/information/10.1587/e80-d_6_672/_p
Copy
@ARTICLE{e80-d_6_672,
author={Rodney WEBSTER, Masaki NAKAGAWA, },
journal={IEICE TRANSACTIONS on Information},
title={An On-Line/Off-Line Compatible Character Recognition Method Based on a Dynamic Model},
year={1997},
volume={E80-D},
number={6},
pages={672-683},
abstract={This paper presents a character recognition method based on a dynamic model, which can be applied to character patterns from both on-line and off-line input. Other similar attempts simply treat on-line patterns as off-line input, while this method makes use of the on-line input's characteristics by representing the time information of handwriting in the character pattern representations. Experiments were carried out on the Hiragana character set. Without non-linear normalization, this method achieved recognition rates of 92.3% for on-line input and 89.1% for off-line input. When non-linear normalization is used, there is an increase in performance for both types of input with on-line input achieving 94.5% and off-line input achieving 94.1%. The reason for the difference in the effectiveness of non-linear normalization on off-line and on-line patterns could be that while the method used for off-line input was an established and proved one, we used our own initial attempt at non-linear normalization for the on-line patterns. If the same level of effectiveness of non-linear normalization as off-line input is achieved on the on-line input, however, the recognition rate for on-line input again improves becoming 96.3%. Since only one standard pattern was used per category for the dictionary patterns, the above results show the promise of this method. This result shows the compatibility of this method to both on-line and off-line input, as well as its effective use of on-line input's characteristics. The effectiveness of this use of the time information is shown by using an actual example. The data also shows the need for a method of non-linear normalization which is more suitable for on-line input.},
keywords={},
doi={},
ISSN={},
month={June},}
Copy
TY - JOUR
TI - An On-Line/Off-Line Compatible Character Recognition Method Based on a Dynamic Model
T2 - IEICE TRANSACTIONS on Information
SP - 672
EP - 683
AU - Rodney WEBSTER
AU - Masaki NAKAGAWA
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E80-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 1997
AB - This paper presents a character recognition method based on a dynamic model, which can be applied to character patterns from both on-line and off-line input. Other similar attempts simply treat on-line patterns as off-line input, while this method makes use of the on-line input's characteristics by representing the time information of handwriting in the character pattern representations. Experiments were carried out on the Hiragana character set. Without non-linear normalization, this method achieved recognition rates of 92.3% for on-line input and 89.1% for off-line input. When non-linear normalization is used, there is an increase in performance for both types of input with on-line input achieving 94.5% and off-line input achieving 94.1%. The reason for the difference in the effectiveness of non-linear normalization on off-line and on-line patterns could be that while the method used for off-line input was an established and proved one, we used our own initial attempt at non-linear normalization for the on-line patterns. If the same level of effectiveness of non-linear normalization as off-line input is achieved on the on-line input, however, the recognition rate for on-line input again improves becoming 96.3%. Since only one standard pattern was used per category for the dictionary patterns, the above results show the promise of this method. This result shows the compatibility of this method to both on-line and off-line input, as well as its effective use of on-line input's characteristics. The effectiveness of this use of the time information is shown by using an actual example. The data also shows the need for a method of non-linear normalization which is more suitable for on-line input.
ER -
English
