Multi-model AI Pipeline for Complex Financial Documents
A multi-model pipeline designed to process complex financial documents with varied structures—deconstructing, translating, and reconstructing them into structured files while maintaining layout and terminology accuracy.
This project focused on automating the conversion of English-language financial reports into editable Korean documents. The main difficulty was not just translation, but accurately breaking down complex report structures and rebuilding them in a usable, high-fidelity format.
These financial documents included complex nested tables, charts, graphs, figures, multi-column layouts, and a mix of scanned and digital pages. Standard OCR or extraction tools could not reliably preserve structure, reading order, or visual relationships between elements. The system needed to segment each document into meaningful parts, understand context, translate finance-specific terminology correctly, and reconstruct the final Korean output with near pixel-perfect layout fidelity.
We built a multi-model document pipeline that deconstructed complex financial PDFs into structured elements, translated each part with context-aware terminology handling, and reconstructed the documents as editable files with high layout accuracy.
- Multi-model pipeline for processing scanned and digital financial PDFs
- Segmentation of tables, charts, graphs, figures, and text regions
- Layout and reading-order reconstruction for complex report structures
- Context-aware translation for finance-specific terminology
- Rebuilding of nested and multi-page tables
- Structured extraction and translation of chart and figure text
- DOCX generation with high visual and structural fidelity
- Editable Korean output instead of flat text extraction
- Python-based orchestration and post-processing workflow
- Reduced manual effort required to convert complex financial reports
- Improved consistency and accuracy in Korean financial terminology
- Preserved complex layouts that would normally require manual rebuilding
- Enabled repeatable, scalable processing of large document volumes
- Created a reusable workflow for future financial document automation projects
Technologies
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