The Transtractor

The Universal Bank Statement PDF Extractor

The Transtractor (Transaction Extractor) is a high-performance, rules-based library for extracting structured transaction data from PDF bank statements issued by different banks and financial institutions. It converts varied statement layouts into validated transaction tables with consistent dates, descriptions, amounts, balances.

The parsing engine is built with Rust and is available through a Python API, browser-based WebAssembly bindings, and an online interface.

Quick Start

Install from PyPI:

pip install transtractor

To parse a bank statement PDF and convert it to CSV:

from transtractor import Parser

# Initialise parser
parser = Parser()

# Convert PDF to CSV
parser.parse('statement.pdf').to_csv('statement.csv')

Writes a CSV of the following standard format regardless of the original statement layout:

date,description,amount,balance
2025-01-01,Transaction 1,50000.0,100000.0
2025-01-01,Transaction 2,-1000.0,99000.0
2025-01-01,Transaction 3,-10000.0,89000.0
2025-01-01,Transaction 4,1350.0,90350.0
2025-01-03,Transaction 5,-530.99,89819.01
2025-01-03,Transaction 6,1532.55,91351.56
2025-01-04,Transaction 7,-568.01,90783.55
2025-01-04,Transaction 8,-23.56,90759.99
...

All columns are included in the CSV output, with the date in ISO format (YYYY-MM-DD), the description as text, and the amount and balance as decimal numbers. Negative amounts indicate debits and positive amounts indicate credits.

Pooling Across Multiple PDFs and Accounts

The following snippet demonstrates how to batch-extract information from all your PDF statements in a single directory:

import csv
from pathlib import Path

from transtractor import Parser

# Paths to the PDF directory and output CSV file
pdf_directory = Path("path/to/your/pdf/directory")
output_csv = Path("output.csv")

parser = Parser()
transactions = set()

# Find and extract PDFs in directory and subdirectories
for pdf_path in sorted(pdf_directory.rglob("*.pdf")):
   print(f"Parsing PDF: {pdf_path}")
   statement_data = parser.parse(str(pdf_path))
   transactions.update(statement_data.transactions)

# Arrange newest to oldest
ordered_transactions = sorted(
   transactions,
   key=lambda transaction: (
      transaction.date,
      transaction.date_index,
      transaction.account_number,
   ),
   reverse=True,
)

# Write the ordered transactions to the output CSV file
with output_csv.open("w", newline="", encoding="utf-8") as csv_file:
   writer = csv.writer(csv_file)
   writer.writerow(("date", "description", "amount", "balance", "account_number"))
   for transaction in ordered_transactions:
      writer.writerow(
            (
               transaction.date,
               transaction.description,
               transaction.amount,
               transaction.balance,
               transaction.account_number,
            )
      )

Web Interface

A user-friendly web interface for bulk extraction is available at transtractor.net.

Supported Banks

See the supported statements page for a full list of supported banks and statement formats. If your bank is not supported, create your own configuration by following the guidelines and load it into the parser:

from transtractor import Parser

parser = Parser()
parser.load('path/to/your_bank_config.json')
parser.parse('your_statement.pdf').to_csv('output.csv')

Community & Support

Website: transtractor.net
PyPI: transtractor
GitHub Repository: weberdak/transtractor-lib
Contributions: weberdak/transtractor-lib/CONTRIBUTING.md

License

The Transtractor is open source software licensed under the MIT License.