# EATEN: Entity-aware Attention for Single Shot Visual Text Extraction

arXiv: https://arxiv.org/abs/1909.09380 · HTML: https://mlsanity.com/paper/1909.09380/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2019.

## Built on by (most cited first, 29 of 29)

- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [StrucTexT: Structured Text Understanding with Multi-Modal Transformers](https://mlsanity.com/paper/2108.02923/) (2021)
- [Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution](https://mlsanity.com/paper/2102.06732/) (2021)
- [Document AI: Benchmarks, Models and Applications](https://mlsanity.com/paper/2111.08609/) (2021)
- [Visual Information Extraction in the Wild: Practical Dataset and End-to-end Solution](https://mlsanity.com/paper/2305.07498/) (2023)
- [ViBERTgrid: A Jointly Trained Multi-Modal 2D Document Representation for Key Information Extraction from Documents](https://mlsanity.com/paper/2105.11672/) (2021)
- [PVC: Progressive Visual Token Compression for Unified Image and Video Processing in Large Vision-Language Models](https://mlsanity.com/paper/2412.09613/) (2024)
- [Entity Relation Extraction as Dependency Parsing in Visually Rich Documents](https://mlsanity.com/paper/2110.09915/) (2021)
- [ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images](https://mlsanity.com/paper/2306.03287/) (2023)
- [DocParser: End-to-end OCR-free Information Extraction from Visually Rich Documents](https://mlsanity.com/paper/2304.12484/) (2023)
- [MatchVIE: Exploiting Match Relevancy between Entities for Visual Information Extraction](https://mlsanity.com/paper/2106.12940/) (2021)
- [Attention Where It Matters: Rethinking Visual Document Understanding with Selective Region Concentration](https://mlsanity.com/paper/2309.01131/) (2023)
- [Efficient OCR for Building a Diverse Digital History](https://mlsanity.com/paper/2304.02737/) (2023)
- [HoVLE: Unleashing the Power of Monolithic Vision-Language Models with Holistic Vision-Language Embedding](https://mlsanity.com/paper/2412.16158/) (2024)
- [Tag, Copy or Predict: A Unified Weakly-Supervised Learning Framework for Visual Information Extraction using Sequences](https://mlsanity.com/paper/2106.10681/) (2021)
- [Qianfan-VL: Domain-Enhanced Universal Vision-Language Models](https://mlsanity.com/paper/2509.18189/) (2025)
- [TRIE++: Towards End-to-End Information Extraction from Visually Rich Documents](https://mlsanity.com/paper/2207.06744/) (2022)
- [PPN: Parallel Pointer-based Network for Key Information Extraction with Complex Layouts](https://mlsanity.com/paper/2307.10551/) (2023)
- [CoMemo: LVLMs Need Image Context with Image Memory](https://mlsanity.com/paper/2506.06279/) (2025)
- [Beyond Human Annotation: Recent Advances in Data Generation Methods for Document Intelligence](https://mlsanity.com/paper/2601.12318/) (2026)
- [Unveiling the Deficiencies of Pre-trained Text-and-Layout Models in Real-world Visually-rich Document Information Extraction](https://mlsanity.com/paper/2402.02379/) (2024)
- [CREPE: Coordinate-Aware End-to-End Document Parser](https://mlsanity.com/paper/2405.00260/) (2024)
- [GraphRevisedIE: Multimodal Information Extraction with Graph-Revised Network](https://mlsanity.com/paper/2410.01160/) (2024)
- [Efficient Medical VIE via Reinforcement Learning](https://mlsanity.com/paper/2506.13363/) (2025)
- [HyperVL: An Efficient and Dynamic Multimodal Large Language Model for Edge Devices](https://mlsanity.com/paper/2512.14052/) (2025)
- [RDU: A Region-based Approach to Form-style Document Understanding](https://mlsanity.com/paper/2206.06890/) (2022)
- [Efficient scene text image super-resolution with semantic guidance](https://mlsanity.com/paper/2403.13330/) (2024)
- [Q-Mask: Query-driven Causal Masks for Text Anchoring in OCR-Oriented Vision-Language Models](https://mlsanity.com/paper/2604.00161/) (2026)
- [Visual Information Extraction from Documents via Classification-Guided Large Vision-Language Models](https://mlsanity.com/paper/2607.22723/) (2026)

## Abstract (arXiv)

Extracting entity from images is a crucial part of many OCR applications, such as entity recognition of cards, invoices, and receipts. Most of the existing works employ classical detection and recognition paradigm. This paper proposes an Entity-aware Attention Text Extraction Network called EATEN, which is an end-to-end trainable system to extract the entities without any post-processing. In the proposed framework, each entity is parsed by its corresponding entity-aware decoder, respectively. Moreover, we innovatively introduce a state transition mechanism which further improves the robustness of entity extraction. In consideration of the absence of public benchmarks, we construct a dataset of almost 0.6 million images in three real-world scenarios (train ticket, passport and business card), which is publicly available at https://github.com/beacandler/EATEN. To the best of our knowledge, EATEN is the first single shot method to extract entities from images. Extensive experiments on these benchmarks demonstrate the state-of-the-art performance of EATEN.