Enhancing Document Key Information Localization Through Data Augmentation
Not on the map. Why?
Abstract (arXiv)
The Visually Rich Form Document Intelligence and Understanding (VRDIU) Track B focuses on the localization of key information in document images. The goal is to develop a method capable of localizing objects in both digital and handwritten documents, using only digital documents for training. This paper presents a simple yet effective approach that includes a document augmentation phase and an object detection phase. Specifically, we augment the training set of digital documents by mimicking the appearance of handwritten documents. Our experiments demonstrate that this pipeline enhances the models' generalization ability and achieves high performance in the competition.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 13 references that are in the map. First posted on arXiv 10 Feb 2025.
Builds on
9 of 13 references.
References · 9
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2017Mask R-CNNcited by 7,277
- 2020DocVQA: A Dataset for VQA on Document Imagescited by 1,317
- 2022LayoutLMv3: Pre-training for Document AI with Unified Text and Image Maskingcited by 485
- 2021Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solutioncited by 64
- 2023DocILE Benchmark for Document Information Localization and Extractioncited by 58
- 2022Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysiscited by 17
- 2022Augraphy: A Data Augmentation Library for Document Imagescited by 14
Built on by
Citing papers per year: 2026: 1.
1 paper.