Hierarchical k-Nearest Neighbor with GPUs and a High Performance Cluster: Application to Handwritten Character Recognition

Abstract

The accelerating progress and availability of low cost computers, high speed networks, and software for high performance distributed computing allow us to reconsider computationally expensive techniques in image processing and pattern recognition. We propose a two-level hierarchical [Formula: see text]-nearest neighbor classifier where the first level uses graphics processor units (GPUs) and the second level uses a high performance cluster (HPC). The system is evaluated on the problem of character recognition with nine databases (Arabic digits, Indian digits (Bangla, Devnagari, and Oriya), Bangla characters, Indonesian characters, Arabic characters, Farsi characters and digits). Contrary to many approaches that tune the model for different scripts, the proposed image classification method is unchanged throughout the evaluation on the nine databases. We show that a hierarchical combination of decisions based on two distances, using GPUs and a HPC provides state-of-the-art performances on several scripts, and provides a better accuracy than more complex systems.