Combining different off-line handwritten character recognizers,10.1109/INES.2011.5954765,Carlos M. Travieso,Jesus B. Alonso,Miguel A. Ferrer

Combining different off-line handwritten character recognizers (Citations: 1)


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This present work presents a recognizer based on the combination of three Support Vector Machine (SVM) classifiers whose inputs have different parameters from characters. The three approaches of feature extraction for handwritten off-line digits, capital letters and lower case letters, have been chosen for improving the combination using database NIST-SD19. We have applied pre-processing in order to achieve greater inter-class discrimination and similarity. These three feature extractions are based on Kirsch masks with and without slant correction and Fourier elliptic descriptors.

Conference: IEEE International Conference on Intelligent Engineering Systems - INES , 2011

DOI: 10.1109/INES.2011.5954765

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Combining different off-line handwritten character recognizers,10.1109/INES.2011.5954765,Carlos M. Travieso,Jesus B. Alonso,Miguel A. Ferrer

References (13)

Using Character Recognition and Segmentation to Tell Computer from Humans (Citations: 22)

Gradient-Based Learning Applied to Document Recognition (Citations: 863)

A trainable feature extractor for handwritten digit recognition (Citations: 33)

KMOD - A Tw o-Parameter SVM Kernel for Pattern Recognition (Citations: 7)

Complementary features combined in an HMM-based system to recognize handwritten digits (Citations: 3)

Citations (1)

Combining different off-line handwritten character recognizers (Citations: 1)


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