# Toward Semi-Supervised Graphical Object Detection in Document Images
###### 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. graphical page objects; object detection; document image analysis; semi-supervised; soft teacher +
Footnote †: journal:  
0000-0001-999-4300]G. K.  
Although defining rules for all graphical objects and running an optical character recognition tool helps localize objects, they cannot generalize to the new objects. This problem has resulted in the usage of deep learning models to detect graphical objects [6; 7; 8; 9]. The deep learning models are rule independent and overcome the generalization problem with high precision. To train a deep learning model, a large amount of annotated data must be available, and as a result, either a manual labeling process or another pre-processing technique must be involved in generating high-quality data, which is both time-consuming and error-prone [10]. Owing to the concerns with training data, the problem changes its state from supervised to semi-supervised. This paper's primary goal is to leverage the unlabeled data in identifying graphical objects without compromising the mean average precision and performance. We leverage the recently proposed Soft Teacher mechanism [10] to design a semi-supervised pipeline for graphical object detection on scanned document images. Figure 1: **Left**: graphical objects Figure, Text and Title. **Right**: graphical objects Table, List Text and Title. Both the images are generated by the Soft Teacher model, which is trained on 10% of the labeled PubLayNet data. Figure 2: **Left**: graphical objects Table and Figure. **Right**: graphical objects Natural image and Signature. Both the images are generated by the the Soft Teacher model, which is trained on 5% of the labeled IIIT-AR-13K data. The semi-supervised deep learning techniques in object detection [11; 12; 13; 14] follow a predefined pipeline where an initial detector is trained using a small amount of available annotated data, and this detector is used to generate pseudo labels for unlabeled data. Finally, the labeled data along with unlabeled data with pseudo labels are used to retrain the model. The quality of these pseudo-labeling approaches depends on the initial model, and a weak initial model may degrade the pseudo-labeling process. To alleviate the weak initial model problem, the end-to-end semi-supervised framework simultaneously performs pseudo labeling for unlabeled data and trains a detector using these pseudo labels with few annotated data in each iteration. The approach trains two models, one for detection known as Student and the other for pseudo labeling known as Teacher. In addition to this, the teacher model is simply an Exponential Moving Average (EMA) of the student model; this ensures that the pseudo-labeling process is constantly updated by the detection process and vice versa.