Cost-Efficient Active Transfer Learning Framework for Object Detection from Engineering Documents

Recently, engineering companies have started to digitise documents in image form to analyse their meaning and extract important content. However, many engineering and contract documents contain different types of components such as texts, tables, and forms, which often hinder accurate interpretation by simple optical character recognition. Therefore, document object detection (DOD) has been studied as a preprocessing step for optical character recognition. Given the ease of acquiring image data, reducing annotation time and effort through transfer learning and active learning has emerged as a key research challenge. In this study, a cost-efficient active transfer learning (ATL) framework for DOD is presented to minimise the effort and cost of time-consuming image annotation for transfer learning. Specifically, three new sample evaluation measures are proposed to enhance the sampling performance of ATL. The proposed framework performed well in ATL experiments of DOD for invitation-to-bid documents. In the experiments, the DOD model was trained on only half of the labelled images, but, in terms of the F1-score, it achieved a similar performance as a DOD model trained on all labelled images. In particular, one of the proposed sampling measures, ambiguity, showed the best sampling performance compared to existing measures, such as entropy and uncertainty. The efficient sample evaluation measures proposed in this study are expected to reduce the time and effort required for ATL.