A MDRNN-SVM hybrid model for cursive offline handwriting recognition. Bezerra, B., Zanchettin, C., & De Andrade, V. Volume 7553 LNCS , 2012. doi abstract bibtex This paper presents a recurrent neural networks applied to handwriting character recognition. The method Multi-dimensional Recurrent Neural Network is evaluated against classical techniques. To improve the model performance we propose the use of specialized Support Vector Machine combined whit the original Multi-dimensional Recurrent Neural Network in cases of confusion letters. The experiments were performed in the C-Cube database and compared with different classifiers. The hierarchical combination presented promising results. © 2012 Springer-Verlag.
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title = {A MDRNN-SVM hybrid model for cursive offline handwriting recognition},
type = {book},
year = {2012},
source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {7553 LNCS},
issue = {PART 2},
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abstract = {This paper presents a recurrent neural networks applied to handwriting character recognition. The method Multi-dimensional Recurrent Neural Network is evaluated against classical techniques. To improve the model performance we propose the use of specialized Support Vector Machine combined whit the original Multi-dimensional Recurrent Neural Network in cases of confusion letters. The experiments were performed in the C-Cube database and compared with different classifiers. The hierarchical combination presented promising results. © 2012 Springer-Verlag.},
bibtype = {book},
author = {Bezerra, B.L.D. and Zanchettin, C. and De Andrade, V.B.},
doi = {10.1007/978-3-642-33266-1_31}
}
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