TrilogyOCR Pipeline: A Multimodal PDF Extraction Solution Based on Mistral Vision Model

TrilogyOCR Pipeline is an end-to-end OCR and multimodal extraction pipeline designed to solve the problem of structured extraction for complex financial documents (such as scanned royalty check PDFs containing tables, handwritten notes) in enterprise scenarios. Combining PyMuPDF, image preprocessing technology, and the Mistral vision model, the solution outputs standardized CSV data, supporting downstream applications like financial analysis and workflow automation, and provides enterprises with a production-ready document processing solution that can be directly deployed.

In enterprise document processing scenarios, a large amount of historical data still exists in the form of scanned PDFs. Traditional OCR solutions struggle to handle financial documents (especially royalty checks) that contain tables, handwritten notes, and various font formats. TrilogyOCR Pipeline is precisely an end-to-end solution designed to address this pain point.

The pipeline adopts a three-layer processing architecture:

1. PDF Parsing Layer: Uses PyMuPDF to extract page content, supporting high-resolution rendering of 200-300 DPI (default 220 DPI);
2. Image Preprocessing Layer: Performs intelligent segmentation with a default 120-pixel overlap to ensure content continuity;
3. Visual Understanding Layer: Invokes the Mistral Vision model (default pixtral-large-latest) for content recognition and structured extraction. The system outputs a fixed-format CSV file (royalty_checks.csv), which can be directly used for financial analysis, workflow integration, and data warehouse import.

To address the challenges of large document processing, the project implements an adaptive segmentation mechanism, including configurations like PAGE_SEGMENT_FALLBACK_PARTS (segment count fallback), PAGE_SEGMENT_OVERLAP_PX (overlap pixels), and SEGMENT_PASS_ALWAYS (force segmentation), ensuring no information is lost when batch processing hundreds of PDF pages. In addition, the system is configured with a fault-tolerant retry mechanism: MISTRAL_MAX_RETRIES=1, RETRY_DELAY_SECONDS=2, which automatically retries when API calls fail, and provides per-page processing time statistics to identify problematic pages.

Web Interface (Recommended)

Execute ./run_web.sh to start the local service with one click. It automatically creates a virtual environment, installs dependencies, loads environment variables, and starts the Flask application (default port 8080), supporting the upload-run-download process and real-time progress display.

Command-Line Batch Processing

Run directly: python trilogy_ocr_pipeline.py --pdf-folder ./checks --output-csv ./royalty_checks.csv --debug, or use the trilogy-ocr command after installation, which is suitable for batch automation scenarios.

Application Scenarios

The solution is applicable to:

• Finance Departments: Batch processing of historical royalty checks, invoices, and statements;
• Legal Teams: Extracting key clauses from scanned contracts;
• Operational Analysis: Converting unstructured documents to structured data;
• Compliance Audits: Establishing traceable processing pipelines and audit logs.

Summary

TrilogyOCR Pipeline combines traditional PDF tools with modern multimodal large models, providing both Web and CLI support. It not only meets the convenience needs of non-technical users but also offers flexible interfaces for automation integration, making it a production-ready solution for organizations dealing with large volumes of scanned financial documents.