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.
@article{pub12439,
author = {
Kallempudi, Goutham
and
Hashmi, Khurram Azeem
and
Pagani, Alain
and
Liwicki, Marcus
and
Stricker, Didier
and
Afzal, Muhammad Zeshan
},
title = {Toward Semi-Supervised Graphical Object Detection in Document Images},
year = {2022},
month = {6},
volume = {14},
number = {6},
pages = {176--198},
journal = {Future Internet (MDPI)},
publisher = {MDPI}
}
German Research Center for Artificial Intelligence Deutsches Forschungszentrum für Künstliche Intelligenz
