Active learning and transfer learning for document segmentation

In this paper, we investigate the effectiveness of classical approaches of active learning in the problem of segmentation of document images in order to reduce the training sample. A modified approach to the selection of images for marking and subsequent training is presented. The results obtained through active learning are compared to transfer learning using fully labeled data. It also investigates how the subject area of the training set, on which the model is initialized for transfer learning, affects the subsequent additional training of the model.