mlsanityData to 14 Oct 2026

Field Extraction from Forms with Unlabeled Data

arXiv 2110.04282 ↗First posted 8 Oct 2021cs.CV cs.AIcited by 4

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Abstract (arXiv)

We propose a novel framework to conduct field extraction from forms with unlabeled data. To bootstrap the training process, we develop a rule-based method for mining noisy pseudo-labels from unlabeled forms. Using the supervisory signal from the pseudo-labels, we extract a discriminative token representation from a transformer-based model by modeling the interaction between text in the form. To prevent the model from overfitting to label noise, we introduce a refinement module based on a progressive pseudo-label ensemble. Experimental results demonstrate the effectiveness of our framework.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 14 of its 23 references that are in the map. First posted on arXiv 8 Oct 2021.

Builds on

14 of 23 references.

References · 14

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  10. 2018Chargrid: Towards Understanding 2D Documentscited by 100
  11. 2019BERTgrid: Contextualized Embedding for 2D Document Representation and Understandingcited by 63
  12. 2020DocStruct: A Multimodal Method to Extract Hierarchy Structure in Document for General Form Understandingcited by 31
2 more
  1. 2018Attend, Copy, Parse -- End-to-end information extraction from documentscited by 25
  2. 2021Form2Seq : A Framework for Higher-Order Form Structure Extractioncited by 16

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Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1; 2025: 1.

4 papers.

  1. 2021Value Retrieval with Arbitrary Queries for Form-like Documentscited by 5
  2. 2024TreeForm: End-to-end Annotation and Evaluation for Form Document Parsingcited by 3
  3. 2023End-to-End Document Classification and Key Information Extraction using Assignment Optimizationcited by 1
  4. 2025Multi-Modal Vision vs. Text-Based Parsing: Benchmarking LLM Strategies for Invoice Processingcited by 1