TY - GEN
T1 - Comparison of smoothing techniques and recognition methods for online Kannada character recognition system
AU - Shwetha, D.
AU - Ramya, S.
N1 - Publisher Copyright:
© 2014 IEEE.
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 - https://www.scopus.com/pages/publications/84988259036
UR - https://www.scopus.com/pages/publications/84988259036#tab=citedBy
U2 - 10.1109/ICAETR.2014.7012888
DO - 10.1109/ICAETR.2014.7012888
M3 - Conference contribution
AN - SCOPUS:84988259036
T3 - 2014 International Conference on Advances in Engineering and Technology Research, ICAETR 2014
BT - 2014 International Conference on Advances in Engineering and Technology Research, ICAETR 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 International Conference on Advances in Engineering and Technology Research, ICAETR 2014
Y2 - 1 August 2014 through 2 August 2014
ER -