# Spatial Dependency Parsing for Semi-Structured Document Information Extraction

arXiv: https://arxiv.org/abs/2005.00642 · HTML: https://mlsanity.com/paper/2005.00642/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2020.

## Built on by (most cited first, 30 of 65)

- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [DocFormer: End-to-End Transformer for Document Understanding](https://mlsanity.com/paper/2106.11539/) (2021)
- [OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations](https://mlsanity.com/paper/2412.07626/) (2024)
- [Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer](https://mlsanity.com/paper/2102.09550/) (2021)
- [BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents](https://mlsanity.com/paper/2108.04539/) (2021)
- [StrucTexT: Structured Text Understanding with Multi-Modal Transformers](https://mlsanity.com/paper/2108.02923/) (2021)
- [OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognition](https://mlsanity.com/paper/2403.19128/) (2024)
- [FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction](https://mlsanity.com/paper/2203.08411/) (2022)
- [Document AI: Benchmarks, Models and Applications](https://mlsanity.com/paper/2111.08609/) (2021)
- [DocILE Benchmark for Document Information Localization and Extraction](https://mlsanity.com/paper/2302.05658/) (2023)
- [A Bounding Box is Worth One Token: Interleaving Layout and Text in a Large Language Model for Document Understanding](https://mlsanity.com/paper/2407.01976/) (2024)
- [DocFormerv2: Local Features for Document Understanding](https://mlsanity.com/paper/2306.01733/) (2023)
- [GeoLayoutLM: Geometric Pre-training for Visual Information Extraction](https://mlsanity.com/paper/2304.10759/) (2023)
- [LMDX: Language Model-based Document Information Extraction and Localization](https://mlsanity.com/paper/2309.10952/) (2023)
- [WebFormer: The Web-page Transformer for Structure Information Extraction](https://mlsanity.com/paper/2202.00217/) (2022)
- [ViBERTgrid: A Jointly Trained Multi-Modal 2D Document Representation for Key Information Extraction from Documents](https://mlsanity.com/paper/2105.11672/) (2021)
- [Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering](https://mlsanity.com/paper/2306.00526/) (2023)
- [Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction](https://mlsanity.com/paper/2310.11016/) (2023)
- [OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models](https://mlsanity.com/paper/2502.16161/) (2025)
- [Deep Learning based Visually Rich Document Content Understanding: A Survey](https://mlsanity.com/paper/2408.01287/) (2024)
- [FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction](https://mlsanity.com/paper/2305.02549/) (2023)
- [Bi-VLDoc: Bidirectional Vision-Language Modeling for Visually-Rich Document Understanding](https://mlsanity.com/paper/2206.13155/) (2022)
- [DocTr: Document Transformer for Structured Information Extraction in Documents](https://mlsanity.com/paper/2307.07929/) (2023)
- [Attention Where It Matters: Rethinking Visual Document Understanding with Selective Region Concentration](https://mlsanity.com/paper/2309.01131/) (2023)
- [Infinity Parser: Layout Aware Reinforcement Learning for Scanned Document Parsing](https://mlsanity.com/paper/2510.15349/) (2025)
- [Cost-effective End-to-end Information Extraction for Semi-structured Document Images](https://mlsanity.com/paper/2104.08041/) (2021)
- [Visually Guided Generative Text-Layout Pre-training for Document Intelligence](https://mlsanity.com/paper/2403.16516/) (2024)
- [Modeling Layout Reading Order as Ordering Relations for Visually-rich Document Understanding](https://mlsanity.com/paper/2409.19672/) (2024)
- [Relational Representation Learning in Visually-Rich Documents](https://mlsanity.com/paper/2205.02411/) (2022)
- [ERNIE-mmLayout: Multi-grained MultiModal Transformer for Document Understanding](https://mlsanity.com/paper/2209.08569/) (2022)

## Abstract (arXiv)

Information Extraction (IE) for semi-structured document images is often approached as a sequence tagging problem by classifying each recognized input token into one of the IOB (Inside, Outside, and Beginning) categories. However, such problem setup has two inherent limitations that (1) it cannot easily handle complex spatial relationships and (2) it is not suitable for highly structured information, which are nevertheless frequently observed in real-world document images. To tackle these issues, we first formulate the IE task as spatial dependency parsing problem that focuses on the relationship among text tokens in the documents. Under this setup, we then propose SPADE (SPAtial DEpendency parser) that models highly complex spatial relationships and an arbitrary number of information layers in the documents in an end-to-end manner. We evaluate it on various kinds of documents such as receipts, name cards, forms, and invoices, and show that it achieves a similar or better performance compared to strong baselines including BERT-based IOB taggger.