Converting a PDF bank statement to Excel or csv can be complicated and time-consuming. This is to be expected because bank statements are designed to be tamper-proof. And they are hard to identify/organize because the file names are usually a string of unintelligible numbers. (Many businesses also look for solutions to rename documents based on the content within each document for convenient identification).

A simple copy-paste from PDF documents will not work. This process gets more hectic when dealing with printed bank statements; as they will additionally need to be scanned!

convert bank statement to excel

Optical Character Recognition (OCR) software, like Nanonets, can convert images, PDFs and other non-editable files into structured editable formats (Excel, csv etc.).

Various OCR software are available today with varying levels of sophistication. The simplest OCR tools just extract the data/text with no attention to the original presentation/order of data. Advanced AI-based OCR software like Nanonets can recognize text, data, tables, graphs and such other elements in documents and only extract relevant data.

Nanonets’ PDF scraper OCR is particularly useful for converting bank statements into machine-readable structured data formats such as excel files (cvs, XML, JSON etc.). Such structured data can be conveniently  included and processed in automated workflows. Automated processing & management of bank statements can streamline a company's financial operations and avoid delays or errors.

Want to extract text from PDF documents or convert PDF table to Excel? Check out Nanonets PDF scraper or PDF parser to scrape PDF data or parse PDFs at scale!

How to Convert Bank Statements to Excel with Nanonets

Here are the detailed steps to create a custom OCR model to convert bank statements from PDF to Excel:

  • Login to Nanonets & select "Create Your Own" to build a custom OCR model
  • Upload sample PDF bank statements to serve as a training set for Nanonets' algorithms
  • Annotate the PDF bank statements to train Nanonets' algorithms to identify the important/relevant data or transactions  in the sample bank statements
  • Build the custom OCR model - Nanonets leverages deep learning to build various OCR models and tests them against each other to pick the most accurate one
  • Test & verify - Add a couple of real bank statements to check whether the custom OCR model works well
  • Export - If the transactions/data have been recognized, extracted and presented correctly, then export the file - download the data extracted from the PDF statements as an Excel, csv, JSON or XML output

Here's a video demo on how to build & train a custom OCR model with Nanonets:

A demo on how to build a custom OCR model with Nanonets

If you’re looking to train/build your own application to convert PDF bank statements to Excel, check out the Nanonets API.

The Nanonets API documentation provides readymade code samples in Shell, Ruby, Golang, Java, C# and Python, as well as detailed API specs for different endpoints.

Details of the process may be obtained here.

Need a smart OCR for image to text, PDF to database, PDF to table, PDF to text, or PDF data extraction? Check out Nanonets' online OCR API in action and start building custom OCR models for free!

Benefits of Converting Bank Statements with Nanonets

Nanonets is ideally placed to convert PDF bank statements into Excel sheets. Its AI-based OCR can convert scanned/PDF statements into structured formats like Excel, XML, csv, JSON, and more.

This helps transform human-readable PDF statements into structured machine-readable digital data & database entries.  

Here are some  specific advantages of using Nanonets to convert bank statements to csv or Excel:

  • Flexibility: Nanonets' deep learning algorithms can easily handle handwritten text, multiple languages, images with low resolution, images with new or cursive fonts and varying sizes, images with shadowy text, tilted text, random unstructured text, image noise, blurred images and many more common data constraints.
  • Customizability: The use of proprietary/custom data to train Nanonets' OCR models helps meet specific business requirements. Bank statement formats differ based on the bank and the type of account.
    • The ability to train OCR models to recognize various formats is ideal for organizations with different kinds of accounts in multiple banks.
  • Adaptability to changes: The possibility to easily re-train existing models with new data allows Nanonets' OCR models to adapt to unforeseen changes.
    • Changing bank document formats or new data capture requirements can thus be easily handled.
  • Detection of tables: Automatic detection of tables including structured row-column information is particularly useful for bank statement digitization.
    • Nanonets offers the facility to export tables to multiple formats like CSV, Excel, & JSON.
  • No post-processing needed: the extraction of relevant data and their automatic sorting into intelligently structured fields minimizes manual post-processing.
  • Works with non-English or multiple languages. This feature is important for multinational operators who work across national borders.
  • Ease of use, batch processing of multiple documents and seamless 2-way integration with multiple accounting software.

Nanonets online OCR & OCR API have many interesting use cases that could optimize your business performance, save costs and boost growth. Find out how Nanonets' use cases can apply to your product.

Update November 2021: this post was originally published in May 2021 and has since been updated multiple times.

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