Abstract
Due to different writing styles and various kinds of noise, the recognition of handwritten numerals is an extremely complicated problem. A new trend to tackle this task by the use of multiple has emerged, which is called combination of multiple classifiers (CME). In this paper, a novel approach for CME is developed and discussed in detail. It contains two steps: data transformation and data classification. In data transformation, the output values of each classifier are first transformed into a form of likeness measurement. In data classification, neural-networks have been found very suitable to aggregate the transformed output and produce the final classification decisions. Experiments on 46,451 handwritten numerals have shown a great improvement in recognition by using the present method.
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