I'm working on creating a Python pipeline that extracts structured financial data from annual reports in PDF format. The goal is to automate the conversion of these documents into usable financial data for modeling and analysis. My ideal workflow involves uploading PDFs, extracting key components like balance sheets and income statements, identifying account numbers and amounts, converting everything into a standardized chart of accounts, and exporting that data in a structured format such as Excel or a database. Given the variability of the PDFs I encounter,
- Some are text-based but have poorly structured tables,
- Others are scanned documents that require OCR,
- And often the layout of key information is inconsistent,
I'm faced with several challenges in ensuring accurate extraction and mapping of data. I'm considering using tools like pdfplumber, PyMuPDF for text extraction, pytesseract for OCR, and pandas for cleaning data, but I'm not sure if this approach is robust enough for the inconsistencies in real-world financial PDFs. I'd appreciate any advice on the best tools for table extraction, whether it's better to run OCR on all PDFs or detect when it's necessary, and suggestions for libraries that are effective for financial data extraction. Moreover, I'm curious if a rule-based or ML-based approach is better for recognizing account numbers. Any insights would be greatly appreciated!
2 Answers
It sounds like you're tackling quite a project! From my experience, a hybrid model that incorporates both rule-based and learning-based approaches tends to yield the best results. For extracting text from standard PDFs, I recommend using pdfplumber—it’s pretty reliable with column alignment. Run a check to see if a page is scanned before invoking OCR, to save on processing time. DocTR works better than pytesseract for dense layouts like financial documents. Also, you might want to use a combination of Camelot for table extraction when grid lines are present, but when they aren’t, machine learning models like LLMs can really shine. They can simplify extracting fields based on a JSON schema. We saw our accuracy jump significantly after using a small model for that.
Yeah, getting consistent results from PDFs can be a nightmare! I feel you on the layout issues—there’s no one-size-fits-all. I'd suggest starting with a two-step process: first, classify each page to figure out if it’s a balance sheet or income statement using a simple classifier. This will help you focus on the specific pages instead of going through the entire document. I've had luck with PyMuPDF for text extraction first, then if the page looks like a scanned one, I use Mistral OCR. That combo works well for tracking down tables in messy documents. Remember, it’s all about narrowing down your focus to improve accuracy.
Thanks for the advice! I’ll definitely check out that classification method to help target the relevant pages.