In this paper, a process of expansion of the training set by synthetic generation of handwritten uppercase letters via deformations of natural images is tested in combination with an approximate k−Nearest Neighbor (k−NN) classifier. It has been previously shown [11] [10] that approximate nearest neighbors search in large databases can be successfully used in an OCR task, and that significant performance improvements can be consistently obtained by simply increasing the size of the training set. In this work, extensive experiments adding distorted characters to the training set are performed, and the results are compared to directly adding new natural samples to the set of prototypes.
Abstract. In this paper we show the results of a performance comparison between two Nearest Neighbour Search Methods: one, proposed by Arya & Mount, is based on a kd−tree data structure and a Branch and Bound approximate search algorithm [1], and the other is a search method based on dimensionality projections, presented by Nene & Nayar in [5]. A number of experiments have been carried out in order to find the best choice to work with high dimensional points and large data sets.
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