mlsanityData to 7 Oct 2026

DocParser: End-to-end OCR-free Information Extraction from Visually Rich Documents

arXiv 2304.12484 ↗First posted 24 Apr 2023cs.CV cs.AIcited by 19

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Abstract (arXiv)

Information Extraction from visually rich documents is a challenging task that has gained a lot of attention in recent years due to its importance in several document-control based applications and its widespread commercial value. The majority of the research work conducted on this topic to date follow a two-step pipeline. First, they read the text using an off-the-shelf Optical Character Recognition (OCR) engine, then, they extract the fields of interest from the obtained text. The main drawback of these approaches is their dependence on an external OCR system, which can negatively impact both performance and computational speed. Recent OCR-free methods were proposed to address the previous issues. Inspired by their promising results, we propose in this paper an OCR-free end-to-end information extraction model named DocParser. It differs from prior end-to-end approaches by its ability to better extract discriminative character features. DocParser achieves state-of-the-art results on various datasets, while still being faster than previous works.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 39 of its 47 references that are in the map. First posted on arXiv 24 Apr 2023.

Builds on

39 of 47 references.

References · 39

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 10; 2025: 6; 2026: 2.

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  1. 2024OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognitioncited by 74
  2. 2025SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversioncited by 51
  3. 2025PACT: Pruning and Clustering-Based Token Reduction for Faster Visual Language Modelscited by 49
  4. 2023Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answeringcited by 24
  5. 2025OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Modelscited by 22
  6. 2025Information Extraction from Visually Rich Documents using LLM-based Organization of Documents into Independent Textual Segmentscited by 9
  7. 2024Deep Learning based Key Information Extraction from Business Documents: Systematic Literature Reviewcited by 6
  8. 2024RealKIE: Five Novel Datasets for Enterprise Key Information Extractioncited by 4
  9. 2024SynthDoc: Bilingual Documents Synthesis for Visual Document Understandingcited by 4
  10. 2025Survey on Question Answering over Visually Rich Documents: Methods, Challenges, and Trendscited by 4
  11. 2026UNIKIE-BENCH: Benchmarking Large Multimodal Models for Key Information Extraction in Visual Documentscited by 4
  12. 2024The future of document indexing: GPT and Donut revolutionize table of content processingcited by 1
7 more
  1. 2024ChatSchema: A pipeline of extracting structured information with Large Multimodal Models based on schemacited by 1
  2. 2024The MERIT Dataset: Modelling and Efficiently Rendering Interpretable Transcriptscited by 1
  3. 2024HIP: Hierarchical Point Modeling and Pre-training for Visual Information Extractioncited by 0
  4. 2024Information Extraction from Heterogeneous Documents without Ground Truth Labels using Synthetic Label Generation and Knowledge Distillationcited by 0
  5. 2024Patchfinder: Leveraging Visual Language Models for Accurate Information Retrieval using Model Uncertaintycited by 0
  6. 2025Pharos-ESG: A Framework for Multimodal Parsing, Contextual Narration, and Hierarchical Labeling of ESG Reportcited by 0
  7. 2026VERSE: Visual Embedding Reduction and Space Exploration. Clustering-Guided Insights for Training Data Enhancement in Visually-Rich Document Understandingcited by 0