Financial Table Extraction in Image Documents
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Abstract (arXiv)
Table extraction has long been a pervasive problem in financial services. This is more challenging in the image domain, where content is locked behind cumbersome pixel format. Luckily, advances in deep learning for image segmentation, OCR, and sequence modeling provides the necessary heavy lifting to achieve impressive results. This paper presents an end-to-end pipeline for identifying, extracting and transcribing tabular content in image documents, while retaining the original spatial relations with high fidelity.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 16 of its 49 references that are in the map. First posted on arXiv 18 Mar 2024.
Builds on
16 of 49 references.
References · 16
- 2017Attention Is All You Needcited by 60,361
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2014Microsoft COCO: Common Objects in Contextcited by 17,608
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2016Densely Connected Convolutional Networkscited by 6,720
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
- 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,512
- 2017Cascade R-CNN: Delving into High Quality Object Detectioncited by 1,276
- 2017Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentationscited by 620
- 2018DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Imagescited by 277
4 more
- 2013How to Construct Deep Recurrent Neural Networkscited by 64
- 2018A Deep Learning Algorithm for One-step Contour Aware Nuclei Segmentation of Histopathological Imagescited by 15
- 2017Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competitioncited by 10
- 2014Locating Tables in Scanned Documents for Reconstructing and Republishing (ICIAfS14)cited by 3
Built on by
Citing papers per year: 2024: 3; 2025: 2.
5 papers.
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- 2024LAW: Legal Agentic Workflows for Custody and Fund Services Contractscited by 6
- 2024FISHNET: Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert Swarms, and Task Planningcited by 5
- 2025FinCriticalED: A Visual Benchmark for Financial Fact-Level OCRcited by 2
- 2025TASER: Table Agents for Schema-guided Extraction and Recommendationcited by 0
- 2025FinCriticalED: A Visual Benchmark for Financial Fact-Level OCR19 Nov 2025
- 2025TASER: Table Agents for Schema-guided Extraction and Recommendation18 Aug 2025
- 2024LAW: Legal Agentic Workflows for Custody and Fund Services Contracts15 Dec 2024
- 2024FISHNET: Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert Swarms, and Task Planning25 Oct 2024
- 2024Is There No Such Thing as a Bad Question? H4R: HalluciBot For Ratiocination, Rewriting, Ranking, and Routing18 Apr 2024