@inproceedings{
 title = {A KNN-SVM hybrid model for cursive handwriting recognition},
 type = {inproceedings},
 year = {2012},
 id = {65ff4511-3677-37d6-9c6a-2e7686462624},
 created = {2019-02-14T18:02:01.407Z},
 file_attached = {false},
 profile_id = {74e7d4ea-3dac-3118-aab9-511a5b337e8f},
 last_modified = {2019-02-14T18:02:01.407Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper presents a hybrid KNN-SVM method for cursive character recognition. Specialized Support Vector Machines (SVMs) are introduced to significantly improve the performance of KNN in handwrite recognition. This hybrid approach is based on the observation that when using KNN in the task of handwritten characters recognition, the correct class is almost always one of the two nearest neighbors of the KNN. Specialized local SVMs are introduced to detect the correct class among these two different classification hypotheses. The hybrid KNN-SVM recognizer showed significant improvement in terms of recognition rate compared with MLP, KNN and a hybrid MLP-SVM approach for a task of character recognition. © 2012 IEEE.},
 bibtype = {inproceedings},
 author = {Zanchettin, C. and Bezerra, B.L.D. and Azevedo, W.W.},
 doi = {10.1109/IJCNN.2012.6252719},
 booktitle = {Proceedings of the International Joint Conference on Neural Networks}
}

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