CSDL Home I ICDAR 2005 Proceedings. Eighth International Conference on Document Analysis and Recognition
Seoul, South Korea
Aug. 31, 2005 to Sept. 1, 2005
Jonathan Alon , Boston University, MA
Vassilis Athitsos , Boston University, MA
Stan Sclaroff , Boston University, MA
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.
Jonathan Alon, Vassilis Athitsos, Stan Sclaroff, "Online and Offline Character Recognition Using Alignment to Prototypes", ICDAR, 2005, Proceedings. Eighth International Conference on Document Analysis and Recognition, Proceedings. Eighth International Conference on Document Analysis and Recognition 2005, pp. 839-845, doi:10.1109/ICDAR.2005.177