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ParseFixer framework takes third in document parsing challenge

By PulseAugur Editorial · [2 sources] ·

Researchers have developed ParseFixer, an agentic framework designed for document parsing challenges. This system achieved third place in the DataMFM Challenge Track 1 by combining a full-page backbone parsing module with an agentic selective correction module. ParseFixer aims to accurately recover textual content and reconstruct document structure by selectively correcting initial parsing failures. AI

IMPACT Demonstrates a novel approach to document parsing, potentially improving structured data extraction from images.

RANK_REASON This is a research paper detailing a system that achieved a specific ranking in a challenge.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · LeKai Yu, Hao Liu, Kun Wang, Zhiran Li, Ruping Cao, Fan Liu, Yupeng Hu ·

    ParseFixer: An Agentic Framework for Document Parsing via Selective Multimodal Correction

    arXiv:2606.11977v1 Announce Type: new Abstract: In this report, we present our third-place solution for the DataMFM Challenge Track 1: Document Parsing. This track requires models to recover structured Markdown documents from document page images while preserving textual content …

  2. arXiv cs.CV TIER_1 English(EN) · Yupeng Hu ·

    ParseFixer: An Agentic Framework for Document Parsing via Selective Multimodal Correction

    In this report, we present our third-place solution for the DataMFM Challenge Track 1: Document Parsing. This track requires models to recover structured Markdown documents from document page images while preserving textual content and document structure. To address the complemen…

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