# ScanBank: A Benchmark Dataset for Figure Extraction from Scanned Electronic Theses and Dissertations

arXiv: https://arxiv.org/abs/2106.15320 · HTML: https://mlsanity.com/paper/2106.15320/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 23 Jun 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment](https://mlsanity.com/paper/1908.06391/) (2019)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks](https://mlsanity.com/paper/1312.6229/) (2013)
- [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019)
- [DocBank: A Benchmark Dataset for Document Layout Analysis](https://mlsanity.com/paper/2006.01038/) (2020)
- [TableBank: A Benchmark Dataset for Table Detection and Recognition](https://mlsanity.com/paper/1903.01949/) (2019)
- [A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector](https://mlsanity.com/paper/1802.09567/) (2018)
- [Extracting Scientific Figures with Distantly Supervised Neural Networks](https://mlsanity.com/paper/1804.02445/) (2018)
- [Video Monitoring Queries](https://mlsanity.com/paper/2002.10537/) (2020)

## Built on by (most cited first, 2 of 2)

- [Beyond Human Annotation: Recent Advances in Data Generation Methods for Document Intelligence](https://mlsanity.com/paper/2601.12318/) (2026)
- [ETDPC: A Multimodality Framework for Classifying Pages in Electronic Theses and Dissertations](https://mlsanity.com/paper/2311.04262/) (2023)

## Abstract (arXiv)

We focus on electronic theses and dissertations (ETDs), aiming to improve access and expand their utility, since more than 6 million are publicly available, and they constitute an important corpus to aid research and education across disciplines. The corpus is growing as new born-digital documents are included, and since millions of older theses and dissertations have been converted to digital form to be disseminated electronically in institutional repositories. In ETDs, as with other scholarly works, figures and tables can communicate a large amount of information in a concise way. Although methods have been proposed for extracting figures and tables from born-digital PDFs, they do not work well with scanned ETDs. Considering this problem, our assessment of state-of-the-art figure extraction systems is that the reason they do not function well on scanned PDFs is that they have only been trained on born-digital documents. To address this limitation, we present ScanBank, a new dataset containing 10 thousand scanned page images, manually labeled by humans as to the presence of the 3.3 thousand figures or tables found therein. We use this dataset to train a deep neural network model based on YOLOv5 to accurately extract figures and tables from scanned ETDs. We pose and answer important research questions aimed at finding better methods for figure extraction from scanned documents. One of those concerns the value for training, of data augmentation techniques applied to born-digital documents which are used to train models better suited for figure extraction from scanned documents. To the best of our knowledge, ScanBank is the first manually annotated dataset for figure and table extraction for scanned ETDs. A YOLOv5-based model, trained on ScanBank, outperforms existing comparable open-source and freely available baseline methods by a considerable margin.