Local learning framework for handwritten character recognition

Abstract

This paper proposes a general local learning framework to effectively alleviate the complexities of classifier design by means of “divide and conquer” principle and ensemble method. The learning framework consists of a quantization layer which uses generalized learning vector quantization (GLVQ) and an ensemble layer which uses multi-layer perceptrons (MLP). The proposed method is tested on public handwritten character data sets, which obtains a promising performance consistently. In contrast to other methods, the proposed method is especially suitable for a large-scale real-world classification problems although it is easily scaled to a small training set while preserving a good performance.