Toward Semi-Supervised Graphical Object Detection in Document Images

The graphical page object detection classifies and localizes objects such as Tables and Figures in a document. As deep learning techniques for object detection become increasingly successful, many supervised deep neural network-based methods have been introduced to recognize graphical objects in documents. However, these models necessitate a substantial amount of labeled data for the training process. This paper presents an end-to-end semi-supervised framework for graphical object detection in scanned document images to address this limitation. Our method is based on a recently proposed Soft Teacher mechanism that examines the effects of small percentage-labeled data on the classification and localization of graphical objects. On both the PubLayNet and the IIIT-AR-13K datasets, the proposed approach outperforms the supervised models by a significant margin in all labeling ratios ( 1 % , 5 % , and 10 % ) . Furthermore, the 10 % PubLayNet Soft Teacher model improves the average precision of Table, Figure, and List by + 5.4 , + 1.2 , and + 3.2 points, respectively, with a similar total mAP as the Faster-RCNN baseline. Moreover, our model trained on 10 % of IIIT-AR-13K labeled data beats the previous fully supervised method + 4.5 points.