Abstract
This paper aimed at working on Online Recognition of Handwritten Kannada Characters. The recognition was done for the Top, Middle and Bottom strokes of Kannada characters. Genius MousePen i608X was used to collect the handwritten character samples to build the database. Handwritten character samples were collected for each character from a particular target-group which includes people who are native to Kannada language and belong to different age groups. These samples were semi-automatically validated, pre-processed and features were extracted. Segmentation of characters was done to divide the strokes into top stroke, middle stroke and bottom stroke. These segmented strokes were individually processed. The pre-processing techniques used in the project include removal of duplicated points, smoothing, interpolating missing points, resampling of points and size normalization. Smoothing techniques was compared for Gaussian and Moving Average Smoothing. Dominant point, writing direction and the curvature features were also extracted. In addition to this, recognition was carried out by KNN and SVM pattern recognition methods and a second level of verification rules was incorporated, yielding a maximum recognition rate of 92.5% for KNN and 94.35% for SVM.
| Original language | English |
|---|---|
| Title of host publication | 2014 International Conference on Advances in Engineering and Technology Research, ICAETR 2014 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781479963935 |
| DOIs | |
| Publication status | Published - 01-01-2014 |
| Externally published | Yes |
| Event | 2014 International Conference on Advances in Engineering and Technology Research, ICAETR 2014 - Unnao, India Duration: 01-08-2014 → 02-08-2014 |
Conference
| Conference | 2014 International Conference on Advances in Engineering and Technology Research, ICAETR 2014 |
|---|---|
| Country | India |
| City | Unnao |
| Period | 01-08-14 → 02-08-14 |
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All Science Journal Classification (ASJC) codes
- Computer Science (miscellaneous)
- Engineering (miscellaneous)
Cite this
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Comparison of smoothing techniques and recognition methods for online Kannada character recognition system. / Shwetha, D.; Ramya, S.
2014 International Conference on Advances in Engineering and Technology Research, ICAETR 2014. Institute of Electrical and Electronics Engineers Inc., 2014. 7012888.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Comparison of smoothing techniques and recognition methods for online Kannada character recognition system
AU - Shwetha, D.
AU - Ramya, S.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - This paper aimed at working on Online Recognition of Handwritten Kannada Characters. The recognition was done for the Top, Middle and Bottom strokes of Kannada characters. Genius MousePen i608X was used to collect the handwritten character samples to build the database. Handwritten character samples were collected for each character from a particular target-group which includes people who are native to Kannada language and belong to different age groups. These samples were semi-automatically validated, pre-processed and features were extracted. Segmentation of characters was done to divide the strokes into top stroke, middle stroke and bottom stroke. These segmented strokes were individually processed. The pre-processing techniques used in the project include removal of duplicated points, smoothing, interpolating missing points, resampling of points and size normalization. Smoothing techniques was compared for Gaussian and Moving Average Smoothing. Dominant point, writing direction and the curvature features were also extracted. In addition to this, recognition was carried out by KNN and SVM pattern recognition methods and a second level of verification rules was incorporated, yielding a maximum recognition rate of 92.5% for KNN and 94.35% for SVM.
AB - This paper aimed at working on Online Recognition of Handwritten Kannada Characters. The recognition was done for the Top, Middle and Bottom strokes of Kannada characters. Genius MousePen i608X was used to collect the handwritten character samples to build the database. Handwritten character samples were collected for each character from a particular target-group which includes people who are native to Kannada language and belong to different age groups. These samples were semi-automatically validated, pre-processed and features were extracted. Segmentation of characters was done to divide the strokes into top stroke, middle stroke and bottom stroke. These segmented strokes were individually processed. The pre-processing techniques used in the project include removal of duplicated points, smoothing, interpolating missing points, resampling of points and size normalization. Smoothing techniques was compared for Gaussian and Moving Average Smoothing. Dominant point, writing direction and the curvature features were also extracted. In addition to this, recognition was carried out by KNN and SVM pattern recognition methods and a second level of verification rules was incorporated, yielding a maximum recognition rate of 92.5% for KNN and 94.35% for SVM.
UR - http://www.scopus.com/inward/record.url?scp=84988259036&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84988259036&partnerID=8YFLogxK
U2 - 10.1109/ICAETR.2014.7012888
DO - 10.1109/ICAETR.2014.7012888
M3 - Conference contribution
BT - 2014 International Conference on Advances in Engineering and Technology Research, ICAETR 2014
PB - Institute of Electrical and Electronics Engineers Inc.
ER -