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In this work, fast approximate nearest neighbours search algorithms are shown to provide high accuracies, similar to those of exact nearest neighbour search, at a fraction of the computational cost in an OCR task. Recent studies [26,15] have shown the power of k-nearest neighbour classifiers (k-nn) using large databases, for character recognition. In those works, the error rate is found to decrease consistently as the size of the database increases. Unfortunately, a large database implies large search times if an exhaustive search algorithm is used. This is often cited as a major problem that limits the practical value of the k-nearest neighbours classification method. The error rates and search times presented in this paper prove, however, that k-nn can be a practical technique for a character recognition task.

Fast and Accurate Handwritten Character Recognition Using Approximate Nearest Neighbours Search on Large Databases