Application of active learning algorithm in handwriting recognition numbers

Active learning is very suitable for many problems in natural language processing, where unlabeled data may be abundant, but annotation is slow and expensive. This article aims to illustrate some active learning methods for handwritten digit recognition tasks, such as the least confidence and entropy methods. We investigated the previously used sequence model query selection strategies and used some selection strategies for sample labeling in handwritten digit recognition. We also conduct a large-scale empirical comparison of using multiple corpora, which shows that our proposed method improves the technical level.