mlsanityData to 7 Oct 2026

Using Neighborhood Context to Improve Information Extraction from Visual Documents Captured on Mobile Phones

arXiv 2108.10395 ↗First posted 23 Aug 2021cs.LG cs.IRcited by 5

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Abstract (arXiv)

Information Extraction from visual documents enables convenient and intelligent assistance to end users. We present a Neighborhood-based Information Extraction (NIE) approach that uses contextual language models and pays attention to the local neighborhood context in the visual documents to improve information extraction accuracy. We collect two different visual document datasets and show that our approach outperforms the state-of-the-art global context-based IE technique. In fact, NIE outperforms existing approaches in both small and large model sizes. Our on-device implementation of NIE on a mobile platform that generally requires small models showcases NIE's usefulness in practical real-world applications.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 11 of its 20 references that are in the map. First posted on arXiv 23 Aug 2021.

Builds on

11 of 20 references.

References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2020Knowledge Distillation: A Surveycited by 1,578
  4. 2019LayoutLM: Pre-training of Text and Layout for Document Image Understandingcited by 549
  5. 2018Chargrid: Towards Understanding 2D Documentscited by 100
  6. 2017Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Networkcited by 92
  7. 2019Graph Convolution for Multimodal Information Extraction from Visually Rich Documentscited by 88
  8. 2018A Survey on Methods and Theories of Quantized Neural Networkscited by 75
  9. 2018GraphIE: A Graph-Based Framework for Information Extractioncited by 45
  10. 2017CloudScan - A configuration-free invoice analysis system using recurrent neural networkscited by 33
  11. 2020Robust Layout-aware IE for Visually Rich Documents with Pre-trained Language Modelscited by 20

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Citing papers per year: 2022: 1; 2023: 4.

5 papers.

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  2. 2023Simple is Better and Large is Not Enough: Towards Ensembling of Foundational Language Modelscited by 1
  3. 2023L3 Ensembles: Lifelong Learning Approach for Ensemble of Foundational Language Modelscited by 1
  4. 2023Explainable and Accurate Natural Language Understanding for Voice Assistants and Beyondcited by 0
  5. 2023I2KD-SLU: An Intra-Inter Knowledge Distillation Framework for Zero-Shot Cross-Lingual Spoken Language Understandingcited by 0