Using moment invariants to recognize and locate partially occluded 2-D objects
Pattern Recognition Letters
Pairwise classification and support vector machines
Advances in kernel methods
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining High-Level Features with Sequential Local Features for On-Line Handwriting Recognition
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume II
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
International Journal on Document Analysis and Recognition
A Fast Feature Selection Model for Online Handwriting Symbol Recognition
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Bayes Classification of Online Arabic Characters by Gibbs Modeling of Class Conditional Densities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Global feature for online character recognition
Pattern Recognition Letters
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Robust handwriting recognition of complex patterns of arbitrary scale, orientation and location is yet elusive to date as reaching a good recognition rate is not trivial for most of the application developments in this field. Cursive scripts with complex character shapes, such as Arabic and Persian, make the recognition task even more challenging. This complexity requires sophisticated representations and learning methods, and comprehensive data samples. A direct approaches to achieve a better performance is focusing on designing more powerful building blocks of a handwriting recognition system which are pattern representation and pattern classification . In this paper we aim to scale up the efficiency of online recognition systems for Arabic characters by integrating novel representation techniques into efficient classification methods. We investigate the idea of incorporating two novel feature representations for online character data. We advocate the usefulness and practicality of these features in classification methods using neural networks and support vector machines. The combinations of proposed representations with related classifiers can offer a module for recognition tasks which can deal with any two-dimensional online pattern. Our empirical results confirm the higher distinctiveness and robustness to character deformations obtained by the proposed representation compared to currently available techniques.