One-Shot Template Matching for Automatic Document Data Capture
Not on the map. Why?
Abstract (arXiv)
In this paper, we propose a novel one-shot template-matching algorithm to automatically capture data from business documents with an aim to minimize manual data entry. Given one annotated document, our algorithm can automatically extract similar data from other documents having the same format. Based on a set of engineered visual and textual features, our method is invariant to changes in position and value. Experiments on a dataset of 595 real invoices demonstrate 86.4% accuracy.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Oct 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 0; 2025: 1.
5 papers.
- 2023DocILE Benchmark for Document Information Localization and Extractioncited by 58
- 2022Business Document Information Extraction: Towards Practical Benchmarkscited by 11
- 2025Document Intelligence in the Era of Large Language Models: A Surveycited by 5
- 2023DocILE 2023 Teaser: Document Information Localization and Extractioncited by 1
- 2021Automatic travel pattern extraction from visa page stamps using CNN modelscited by 0
- 2025Document Intelligence in the Era of Large Language Models: A Survey15 Oct 2025
- 2023DocILE Benchmark for Document Information Localization and Extraction11 Feb 2023
- 2023DocILE 2023 Teaser: Document Information Localization and Extraction29 Jan 2023
- 2022Business Document Information Extraction: Towards Practical Benchmarks20 Jun 2022
- 2021Automatic travel pattern extraction from visa page stamps using CNN models1 Dec 2021