How to Extract Text from PDF Financial Statements?

Extracting text from PDF financial statements is crucial for analysts, accountants, and businesses needing structured data for reporting. This guide covers the best methods for extracting text from editable and scanned PDFs, ensuring accuracy and efficiency.

Why Is Extracting Text From PDF Financial Statements Important?

Extracting text from PDF financial statements is important because it simplifies automated analysis, data processing, and reporting. It saves time on manual data entry and reduces errors, improving financial decision-making and compliance reporting.

1. Easy Data Analysis

Extracting text from PDFs turns unstructured data into a structured format, making it easier to analyze, compare, and visualize. This reduces manual work and errors, leading to more accurate decision-making.

2. Automation of Financial Workflow

By integrating extracted data into financial software, text extraction automates tasks like reporting, auditing, and forecasting. This enhances efficiency, reduces manual effort, and improves accuracy in the financial workflow.

3. Accurate Record-Keeping

Text extraction ensures that data is stored accurately and consistently, minimizing errors. It also simplifies data retrieval and helps businesses stay compliant with financial regulations, improving transparency and reliability.

4. Quick Comparisons

Extracting text allows for fast comparisons of financial data across different periods or companies. This helps identify trends, evaluate performance, and make informed business decisions with ease.

Understanding The Type Of PDF Financial Statement

Before you start extracting text, you need to determine whether your PDF is text-based or scanned.

• Text-Based PDF: These PDFs contain selectable, searchable text, usually created from digital sources like Word documents or spreadsheets.
• Scanned PDF: These PDFs are images of physical documents and are not searchable or selectable unless OCR is used to convert them into editable text.

How to check?

• Try selecting text in the PDF. If you can copy it, it is a text-based PDF.
• If you cannot select the text, it is a scanned PDF, which requires OCR (Optical Character Recognition).

Extracting Text From PDF Financial Statements

Step 1: Extracting Text from Editable PDFs

If your PDF contains selectable text, use one of these methods:

• Copy-Paste Method: Open the PDF and manually copy the required text for basic extraction.
• Built-in Export Option: Use the PDF reader’s export feature to convert the file into an editable format like Excel or Word.
• Online Conversion Tools: Various online tools allow text extraction by converting PDFs into editable formats.

 Limitations: Manual copy-pasting is inefficient for large reports. Formatting may be lost.

Step 2: Extracting Text from Scanned PDFs (Using OCR)

Scanned PDFs contain images, not selectable text, requiring OCR technology to extract and convert characters into machine-readable text. This enables editing, searching, and data extraction, making digitization, automation, and accessibility easier.

Step 3: Extracting Text Using Python (For Advanced Users)

The next method that you can implement is using Python scripts for exporting the data from financial PDFs. For this, Python offers excellent libraries:

Using PyPDF2 (For Text-Based PDFs)

import PyPDF2

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

pdf_reader = PyPDF2.PdfReader(pdf_file)

for page in pdf_reader.pages:

    print(page.extract_text())

pdf_file.close()

Using Tesseract OCR (For Scanned PDFs)

from pytesseract import image_to_string
from pdf2image import convert_from_path

images = convert_from_path('financial_statement.pdf')
for image in images:
    text = image_to_string(image)  # Fixed the typo here
    print(text)

Pro Tip: Tesseract OCR is free but requires setup.

Step 4: Extracting Tables And Structured Data From PDFs

Extracting tables from PDFs organizes financial reports, invoices, and datasets efficiently, converting them into editable formats for easy analysis, comparison, and integration, ensuring accuracy and saving time.

Using Tabula (Python Code Example) Acrobat 

import tabula

file_path = "financial_statement.pdf"

tables = tabula.read_pdf(file_path, pages='all')

print(tables)

 Note: Tabula works best with properly formatted tables.

Common Challenges And Solutions

• Low-quality scanned PDFs? → If a scanned PDF is blurry or low in resolution, OCR may struggle to recognize the text accurately. Using high-resolution scans improves text clarity and ensures better extraction results.

• Formatting issues after extraction? → Sometimes, extracted text loses its original structure, especially in tables. Using structured tools like Tabula helps maintain proper formatting and keeps the data organized.

• Numbers and symbols misread by OCR? → OCR may misinterpret numbers, symbols, or special characters in financial data. It’s important to manually review and correct key figures to ensure accuracy.

Conclusion

Extracting text from PDF financial statements is essential for efficient data processing, analysis, and reporting. It can be done manually for small tasks, using OCR for scanned documents, or through automation for greater accuracy and speed. 

AI-powered tools enhance workflows, reduce errors, and provide structured data, improving decision-making. Automating this process ensures efficiency, compliance, and better financial management.

Frequently Asked Questions