Nice survey paper on Document parsing that has evolved from rigid pipelines to flexible end-to-end neural architectures
🎯 Document parsing - converting unstructured/semi-structured documents (contracts, papers, invoices) into machine-readable formats faces challenges in accurately extracting text, tables, math expressions while preserving relationships between elements.
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📚 https://arxiv.org/abs/2410.21169
🔧 Methods explored in this Paper:
• Two main approaches:
- Modular Pipeline: Separate modules for layout analysis, content extraction, relation integration
- End-to-end Vision-Language Models: Process visual/textual data simultaneously
• Key Components:
- Layout Analysis: Detects structural elements using CNN/Transformer architectures
- Content Extraction: OCR for text, math expressions, tables using specialized models
- Relation Integration: Preserves spatial/semantic relationships between elements
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💡 Key Insights:
• Recent models like Nougat, Fox, GOT show promise in end-to-end document processing
• Integration of visual-language models improves semantic understanding
• Challenge remains in handling high-density text and complex layouts
• Need for larger, more diverse datasets beyond academic papers
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