Analyzing the Potential of Active Learning for Document Image Classification

Abstract

Deep learning has been extensively researched in the field of document analysis and has shown excellent performance across a wide range of document-related tasks. As a result, a great deal of emphasis is now being placed on its practical deployment and integration into modern industrial document processing pipelines. It is well-known, however, that deep learning models are data-hungry and often require huge volumes of annotated data in order to achieve competitive performances. And since data annotation is a costly and labor-intensive process, it remains one of the major hurdles to their practical deployment. This study investigates the possibility of using active learning to reduce the costs of data annotation in the context of Document Image Classification, which is one of the core components of modern document processing pipelines. The results of this study demonstrate that by utilizing active learning (AL), deep document classification models can achieve competitive performances to the models trained on fully annotated datasets, and in some cases, even surpass them by annotating only 15%-40% of the total training dataset. Furthermore, this study demonstrates that modern AL strategies significantly outperform random querying, and in many cases achieve comparable performance to the models trained on fully annotated datasets even in the presence of practical deployment issues such as data imbalance, and annotation noise, and thus, offer tremendous benefits in real-world deployment of deep document classification models. The code to reproduce our experiments is publicly available at https://github.com/saifullah3396/doc_al.

Threshold U-Net: speed up document binarization with adaptive thresholds

“…The other one approach was presented in [15] based on cold diffusion models. The main idea of the proposed method is to train a deep propagation network for document binarization in two stages: a forward propagation stage and a backward recovery stage.…”