Toward Semi-Supervised Graphical Object Detection in Document Images

Goutham Kallempudi, Khurram Azeem Hashmi, Alain Pagani, Marcus Liwicki, Didier Stricker and Muhammad Zeshan Afzal
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Goutham Kallempudi: Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany
Khurram Azeem Hashmi: Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany
Alain Pagani: German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany
Marcus Liwicki: Department of Computer Science, Luleå University of Technology, 97187 Lulea, Sweden
Didier Stricker: Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany
Muhammad Zeshan Afzal: Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany

Future Internet, 2022, vol. 14, issue 6, 1-21

Abstract: 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.

Keywords: graphical page objects; object detection; document image analysis; semi-supervised; soft teacher (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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