Discriminant Substrokes for Online Handwriting Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
An Approach to Identify Unique Styles in Online Handwriting Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Machine Recognition of Online Handwritten Devanagari Characters
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
LipiTk: a generic toolkit for online handwriting recognition
ACM SIGGRAPH 2007 courses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Divide and conquer technique in online handwritten Kannada character recognition
Proceedings of the International Workshop on Multilingual OCR
Resolving Ambiguities in Confused Online Tamil Characters with Post Processing Algorithms
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
International Journal of Applied Mathematics and Computer Science
A statistical-topological feature combination for recognition of handwritten numerals
Applied Soft Computing
Language models for online handwritten Tamil word recognition
Proceeding of the workshop on Document Analysis and Recognition
Proceeding of the workshop on Document Analysis and Recognition
Hi-index | 0.00 |
In this paper, Principal Component Analysis (PCA) is applied to the problem of Online Handwritten Character Recognition in the Tamil script. The input is a temporally ordered sequence of (x,y) pen coordinates corresponding to an isolated character obtained from a digitizer. The input is converted into a feature vector of constant dimensions following smoothing and normalization. PCA is used to find the basis vectors of each class subspace and the orthogonal distance to the subspaces used for classification. Pre-clustering of the training data and modification of distance measure are explored to overcome some common problems in the traditional subspace method. In empirical evaluation, these PCA-based classification schemes are found to compare favorably with nearest neighbour classification.