Handwritten Character Recognition Using Active Semi-supervised Learning

Constructing a handwritten character recognition model is considered challenging partly due to the high variety of handwriting styles and the limited amount of training data. In practice, only a handful of labeled examples from limited number of writers are provided during the training of the model. Still, a large collection of already available unlabeled handwritten character data from several sources are often left unused. To alleviate the problem of small training sample size, we propose a graph-based active semi-supervised learning approach for handwritten character recognizer construction. The method iteratively builds a neighborhood graph of all examples including the unlabeled ones, assigns pseudo labels to the unlabeled data and retrains the model. Additionally, the label of the least confident pseudo label according to a newly proposed uncertainty measure is to be requested from the oracle. Experiments on NIST handwritten digits dataset demonstrated that the proposed learning method better utilizes the unlabeled data compared to existing approaches as measured by recognition accuracy. In addition, our active learning strategy is also more effective compared to baseline strategies.