Comparison of smoothing techniques and recognition methods for online Kannada character recognition system

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...

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Bibliographic Details
Published in:2014 International Conference on Advances in Engineering & Technology Research (ICAETR - 2014) pp. 1 - 5
Main Authors: Shwetha, D., Ramya, S.
Format: Conference Proceeding
Language:English
Published: IEEE 01.08.2014
Subjects:
Accuracy
Character recognition
Handwriting recognition
Handwritten Kannada Characters
Kernel
KNN
Smoothing Algorithms
Smoothing methods
Support vector machines
SVM
Writing
ISSN:2347-9337
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Summary: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.
ISSN:2347-9337
DOI:10.1109/ICAETR.2014.7012888