EATEN: Entity-Aware Attention for Single Shot Visual Text Extraction

He, Guotian; Qin, Xiameng; Liu, Jiaming; Han, Junyu; Liu, Jingtuo; Ding, Errui

doi:10.1109/icdar.2019.00049

Cited by 39 publications

(10 citation statements)

References 22 publications

(31 reference statements)

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“…DeepForm (Stray & Svetlichnaya, 2020) is an English dataset for the disclosure form of political advertisements on television. The EATEN dataset (Guo et al, 2019) is a dataset for information extraction of Chinese documents. further add text box annotations to the 400 subset of EATEN.…”

Section: Visual Information Extractionmentioning

confidence: 99%

Document AI: Benchmarks, Models and Applications

Cui¹,

Xu²,

Lv³

et al. 2021

Preprint

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Document AI, or Document Intelligence, is a relatively new research topic that refers to the techniques for automatically reading, understanding, and analyzing business documents. It is an important research direction for natural language processing and computer vision. In recent years, the popularity of deep learning technology has greatly advanced the development of Document AI, such as document layout analysis, visual information extraction, document visual question answering, document image classification, etc. This paper briefly reviews some of the representative models, tasks, and benchmark datasets. Furthermore, we also introduce early-stage heuristic rule-based document analysis, statistical machine learning algorithms, and deep learning approaches especially pre-training methods. Finally, we look into future directions for Document AI research.

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Section: Visual Information Extractionmentioning

confidence: 99%

Document AI: Benchmarks, Models and Applications

Cui¹,

Xu²,

Lv³

et al. 2021

Preprint

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“…Visually rich document understanding includes many tasks, such as layout recognization (Zhong et al, 2019b;Li et al, 2020), table detection and recognition (Li et al, 2019a;Zhong et al, 2019a) and key information extraction (Graliński et al, 2020;Guo et al, 2019;Huang et al, 2019;G. Jaume and Thiran, 2019;Majumder et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

Entity Relation Extraction as Dependency Parsing in Visually Rich Documents

Zhang¹,

Zhang²,

Wang³

et al. 2021

Preprint

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Previous works on key information extraction from visually rich documents (VRDs) mainly focus on labeling the text within each bounding box (i.e., semantic entity), while the relations in-between are largely unexplored. In this paper, we adapt the popular dependency parsing model, the biaffine parser, to this entity relation extraction task. Being different from the original dependency parsing model which recognizes dependency relations between words, we identify relations between groups of words with layout information instead. We have compared different representations of the semantic entity, different VRD encoders, and different relation decoders. For the model training, we explore multi-task learning to combine entity labeling and relation extraction tasks; and for the evaluation, we conduct experiments on different datasets with filtering and augmentation. The results demonstrate that our proposed model achieves 65.96% F1 score on the FUNSD dataset. As for the realworld application, our model has been applied to the in-house customs data, achieving reliable performance in the production setting. * Corresponding author. The author's contributions were carried out while at Alibaba Group. His current affiliation is Vipshop (China) Co., Ltd.

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“…Two related concurrent works were presented in [3,12]. [12] proposed an entity-aware attention text extraction network to extract entities from VRDs.…”

Section: Information Extractionmentioning

confidence: 99%

“…Two related concurrent works were presented in [3,12]. [12] proposed an entity-aware attention text extraction network to extract entities from VRDs. However, it could only process documents of relatively fixed layout and structured text, like train tickets, passports and bussiness cards.…”

Section: Information Extractionmentioning

confidence: 99%

TRIE: End-to-End Text Reading and Information Extraction for Document Understanding

Zhang¹,

Xu²,

Cheng³

et al. 2020

Preprint

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Since real-world ubiquitous documents (e.g., invoices, tickets, resumes and leaflets) contain rich information, automatic document image understanding has become a hot topic. Most existing works decouple the problem into two separate tasks, (1) text reading for detecting and recognizing texts in the images and ( 2) information extraction for analyzing and extracting key elements from previously extracted plain text. However, they mainly focus on improving information extraction task, while neglecting the fact that text reading and information extraction are mutually correlated. In this paper, we propose a unified end-to-end text reading and information extraction network, where the two tasks can reinforce each other. Specifically, the multimodal visual and textual features of text reading are fused for information extraction and in turn, the semantics in information extraction contribute to the optimization of text reading. On three real-world datasets with diverse document images (from fixed layout to variable layout, from structured text to semi-structured text), our proposed method significantly outperforms the state-of-the-art methods in both efficiency and accuracy.

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EATEN: Entity-Aware Attention for Single Shot Visual Text Extraction

Cited by 39 publications

References 22 publications

Document AI: Benchmarks, Models and Applications

Document AI: Benchmarks, Models and Applications

Entity Relation Extraction as Dependency Parsing in Visually Rich Documents

TRIE: End-to-End Text Reading and Information Extraction for Document Understanding

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