Why PDF Financial Statement Text Extraction Fails & How To Fix It?

Extracting text from PDF financial statements is a common challenge for businesses, accountants, and analysts. Issues like formatting problems and scanned PDFs often hinder accurate text extraction. This article explores why extraction fails and how to resolve it.

If you struggle to extract text value from a PDF financial statement, this guide will help you understand common challenges and effective solutions.

Why PDF Financial Statement Text Extraction Fails?

Text extraction from PDF financial statements is challenging due to formatting, scanned images, and embedded fonts. OCR tools, AI parsers, and structured methods can help overcome these issues.

1. Scanned PDFs Instead of Digital PDFs

Many financial statements are scanned documents rather than digitally created PDFs. In these cases, the text is stored as an image rather than selectable text, making it impossible to copy and extract directly.

Solution: Use optical character recognition software like Adobe Acrobat, Tesseract OCR, or online OCR tools to convert scanned PDFs into readable text.

2. Complex Formatting and Tables

Financial statements often contain complex layouts with tables, columns, and numbers. Traditional text extraction methods struggle to maintain the correct structure, leading to missing or jumbled data.

Solution: Use PDF extraction tools that support table detection, such as Tabula, Smallpdf, or AI-based tools like Docparser.

3. Text Encoding Issues

Some PDFs use embedded fonts or encoding techniques that make the text unreadable when extracted. This happens when PDFs store text as graphical objects rather than standard text.

Solution: Try opening the PDF in a text-friendly format (e.g., Word or Notepad) to check if the text is selectable. If not, use OCR software to convert the document.

4. Security Restrictions & Password Protection

Some financial statements have security settings that prevent copying, editing, or extracting text.

Solution: If you have permission, use a PDF unlocker tool or request an editable version from the source. Adobe Acrobat Pro can also help remove restrictions.

5. Multiple Columns and Headers Misalignment

Many financial statements have multicolumn layouts that standard text extractors misinterpret, leading to disorganized data.

Solution: To extract structured text correctly, use specialized PDF extraction tools like Camelot or PyPDF2 (for Python users).

How To Fix PDF Financial Statement Text Extraction Issues?

Extracting text from financial statements is tough due to complex formatting. Using AI-powered OCR tools, converting to structured formats, and applying NLP techniques can improve accuracy and organization.

Step 1: Identify the Type of PDF

Before extracting text from a PDF, it’s crucial to identify its type, as different PDFs require different extraction techniques. There are three main types:

1. Text-based PDFs – These contain selectable text and are the easiest to extract using standard parsing tools like Python’s PyPDF2 or PDFMiner.
   
2. Image-based PDFs (Scanned PDFs) – These are scanned documents where text is stored as an image, requiring OCR (Optical Character Recognition) tools like Tesseract or Adobe Acrobat to convert images into machine-readable text.
   
3. Hybrid PDFs – These contain both text and images, making extraction more complex. A combination of text parsers and OCR is often needed to ensure accurate data retrieval.

Step 3: Extract Text Using OCR 

Scanned PDFs store content as images, not text, so OCR is used to convert them into machine-readable text.

To extract text from scanned PDFs, follow these steps:

1. Preprocess the Image – Enhance clarity by adjusting contrast, removing noise, and converting to grayscale.
2. Apply OCR – Use an OCR engine to detect and recognize characters within the scanned document.
3. Post-Processing – Clean extracted text by correcting misrecognized characters and structuring data for better readability.

Step 4: Extract Text Using Python 

Python offers powerful libraries for extracting text from PDFs. For text-based PDFs, use PyPDF2 or PDFMiner, while for scanned PDFs, apply OCR with Tesseract. Example:

import PyPDF2

pdf_file = open('financial_statement.pdf', 'rb')

reader = PyPDF2.PdfReader(pdf_file)

for the reader page.pages:

    print(page.extract_text())

pdf_file.close()

This script extracts text from a PDF page by page.

Step 5: Clean and Format the Extracted Data

After extracting text from a PDF, the raw output may contain extra spaces, line breaks, misrecognized characters, or unstructured data. Cleaning and formatting improve readability and usability.

Steps to clean extracted data:

1. Remove unwanted spaces & line breaks – Use string manipulation or regex to clean irregular spacing.
2. Fix OCR errors – Correct misrecognized characters using text processing techniques.
3. Structure data – Convert extracted text into a structured format like CSV, JSON, or a database table for easy analysis.

Conclusion

Extracting text from PDF financial statements can be challenging due to scanned documents, formatting issues, encoding problems, and security restrictions. However, using OCR tools, specialized PDF extractors, and automation scripts, you can effectively retrieve structured data.

If you frequently need to extract text value from a PDF financial statement, consider using AI-powered solutions for better accuracy and efficiency.

Frequently Asked Questions