Online and Offline Character Recognition Using Alignment to Prototypes


Autoria(s): Alon, Jonathan; Athitsos, Vassilis; Sclaroff, Stan
Data(s)

20/10/2011

20/10/2011

03/06/2005

Resumo

Nearest neighbor classifiers are simple to implement, yet they can model complex non-parametric distributions, and provide state-of-the-art recognition accuracy in OCR databases. At the same time, they may be too slow for practical character recognition, especially when they rely on similarity measures that require computationally expensive pairwise alignments between characters. This paper proposes an efficient method for computing an approximate similarity score between two characters based on their exact alignment to a small number of prototypes. The proposed method is applied to both online and offline character recognition, where similarity is based on widely used and computationally expensive alignment methods, i.e., Dynamic Time Warping and the Hungarian method respectively. In both cases significant recognition speedup is obtained at the expense of only a minor increase in recognition error.

Office of Naval Research (N00014-03-1-0108); National Science Foundation (IIS-0308213, EIA-0202067)

Identificador

http://hdl.handle.net/2144/1845

Idioma(s)

en_US

Publicador

Boston University Computer Science Department

Relação

BUCS Technical Reports;BUCS-TR-2005-019

Tipo

Technical Report