Multiple Instance Learning Based Method for Similar Handwritten Chinese Characters Discrimination

This paper proposes a Multiple Instance Learning based method for similar handwritten Chinese characters discrimination. The similar handwritten Chinese characters recognition problem is first defined as a Multiple-instance learning problem. Then the problem is solved by the AdaBoost framework. The proposed method selects some self-adapting critical regions as weak classifiers, and therefore it is more suitable for the wide variability of writing styles. Our experimental results demonstrate that the proposed method outperforms the other state-of-the-art methods.