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Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 2
An Efficient Feature Extraction and Dimensionality Reduction Scheme for Isolated Greek Handwritten Character Recognition
Curitiba, Parana, Brazil
September 23-September 26
ISBN: 0-7695-2822-8
| ASCII Text | x | ||
| G. Vamvakas, B. Gatos, S. Petridis, N. Stamatopoulos, "An Efficient Feature Extraction and Dimensionality Reduction Scheme for Isolated Greek Handwritten Character Recognition," Document Analysis and Recognition, International Conference on, vol. 2, pp. 1073-1077, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 2, 2007. | |||
| BibTex | x | ||
| @article{ 10.1109/ICDAR.2007.49, author = {G. Vamvakas and B. Gatos and S. Petridis and N. Stamatopoulos}, title = {An Efficient Feature Extraction and Dimensionality Reduction Scheme for Isolated Greek Handwritten Character Recognition}, journal ={Document Analysis and Recognition, International Conference on}, volume = {2}, year = {2007}, issn = {1520-5363}, pages = {1073-1077}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDAR.2007.49}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Document Analysis and Recognition, International Conference on TI - An Efficient Feature Extraction and Dimensionality Reduction Scheme for Isolated Greek Handwritten Character Recognition SN - 1520-5363 SP1073 EP1077 A1 - G. Vamvakas, A1 - B. Gatos, A1 - S. Petridis, A1 - N. Stamatopoulos, PY - 2007 KW - null VL - 2 JA - Document Analysis and Recognition, International Conference on ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDAR.2007.49
In this paper, we present an off-line methodology for isolated Greek handwritten character recognition based on efficient feature extraction followed by a suitable feature vector dimensionality reduction scheme. Extracted features are based on (i) horizontal and vertical zones, (ii) the projections of the character profiles, (iii) distances from the character boundaries and (iv) profiles from the character edges. The combination of these types of features leads to a 325- dimensional feature vector. At a next step, a dimensionality reduction technique is applied, according to which the dimension of the feature space is lowered down to comprise only the features pertinent to the discrimination of characters into the given set of letters. In this paper, we also present a new Greek handwritten database of 36,960 characters that we created in order to measure the performance of the proposed methodology.
Citation:
G. Vamvakas, B. Gatos, S. Petridis, N. Stamatopoulos, "An Efficient Feature Extraction and Dimensionality Reduction Scheme for Isolated Greek Handwritten Character Recognition," icdar, vol. 2, pp.1073-1077, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 2, 2007
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