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