ARTICLE AD BOX
I am looking for the best open-source models, frameworks, or libraries for extracting text and structured data from PDFs with high accuracy.
My use case includes:
Extracting text from scanned and digital PDFs
Handling invoices, forms, insurance documents, and reports
Preserving table structure and layout
OCR support for low-quality scans
Detecting key-value pairs and fields accurately
Currently, my workflow is:
Extract raw text from PDFs using tools like PyMuPDF/pdfplumber
Use regular expressions (regex) to identify and extract required fields
The problem is that for every new document format, I need to write separate regex patterns and custom extraction logic. Maintaining these regex queries is becoming very complex and difficult to scale for real-world production systems with multiple document formats.
Because of this, I am planning to move toward AI/LLM-based document understanding models that can intelligently identify fields and structured data without relying heavily on manual regex rules.
I am currently exploring solutions like:
PyMuPDF
pdfplumber
Tesseract OCR
However, I would like to know which open-source models or combinations provide the best real-world accuracy and performance for production-level PDF extraction systems.
Questions:
Which open-source models currently provide the highest accuracy for PDF extraction?
Is there any recommended pipeline for handling both scanned and digital PDFs?
Which models work best for table extraction and document understanding?
Are there any lightweight models suitable for deployment on local servers?
Has anyone successfully replaced regex-heavy extraction systems using AI models?
What are the current best practices for building a robust AI-based PDF extraction workflow?
Tech stack preference:
Python
Hugging Face models
OCR + LLM/document understanding approaches
Any suggestions, benchmarks, architecture recommendations, or production experiences would be very helpful.
