Handy Tools to Convert PDF Bank Statements into CSV
Convert PDF bank statements to CSV with the list of tools that we've compiled for you to automate various verification processes and workflows.
Learn more about the common use cases of PDF data extraction and how you can extract data from PDF files with manual or automated methods.
PDF, or Portable Document Format, has been used in all areas since it is the more efficient, reliable, and cost-effective replacement of paper. Valuable information is stored in PDF and more and more PDF files are being created and exchanged every day. However, such a convenient file format also has its drawback when it comes to data extraction. To help you easily and efficiently extract data from PDF files, we will be discussing:
The biggest problem with data trapped in PDF files is that the data is unstructured, meaning that the information is not organized according to specified parameters. Although these PDF files are very easy for us to read, computers and most programmes can neither understand nor process them.
If you wish to analyze the data from several receipts, you will need to format all the information into a relational database, which is a structured database that allows computers to recognize the relations between the stored information, or table as shown below.
However, most of the time, PDF files often contain images from which traditional programs cannot extract data. Even if the PDF files have some texts or tables in them, directly copy-pasting them into an Excel spreadsheet can still result in some formatting and order issues.
Since PDF was introduced in the early 1990s, all kinds of businesses, such as finance, logistics, retail, and more, have used it as the main format for internal and external information exchange. PDF files can contain important business data in the forms of texts, tables, and even images. Common documents digitized into PDF files include:
Piles of documents with important business information can now be digitized into one single file and sent to others within seconds. However, you usually cannot edit the content in the PDF files since very often PDF files contain images of scanned documents.
This is probably the go to solution when dealing with a small number of PDF files. If your PDF files contain images, you just open the files, rekey everything from the images to a spreadsheet or even create a json file.
Although this method is scalable as you can always hire more people to complete the tasks, it is error-prone and inefficient. Furthermore, your employees will waste precious time on repetitive tasks when they could be doing other more strategic and productive tasks.
In the long run, extracting data from PDF through in-house manual data entry can be quite costly and is certainly not ideal. Outsourcing it to other companies might reduce the cost and allow employees to focus on their original tasks. However, there are a few common issues with outsourcing manual data entry such as quality control and data security as you are giving business data to a third party and asking them to handle the task.
Involving PDF converters can speed up the in-house manual data entry process as employees will not have to rekey everything from scratch. PDF converters utilize Optical Character Recognition (OCR) engines to convert images into machine-coded texts that can be edited. It comes in different forms, such as on-premises or web-based software.
To extract the data from PDF files, using OCR technologies without layout extraction AI might generate results that are just texts without organization.
Take this sample Purchase Order from TemplateLab:
After you extract the data using OCR technologies, the result will look like:
You can then copy-paste the texts to format them into structured data. However, PDF converters usually cannot process batches of PDF files. If you want to compute the sum of 10 purchase orders, you will have to parse the PDF and copy-paste the subtotals one by one to a spreadsheet, which is certainly not the most efficient solution. Intelligent document processing (IDP) solution, on the other hand, can solve the problem.
Compared to the aforementioned methods, IDP solutions, which can automatically extract data from batches of images of PDF files, like FormX are more scalable, cost-effective, efficient and accurate.
FormX comes with a set of pre-trained templates, such as receipts, business registration, food license, passport, Hong Kong ID, etc., for users to extract data from images or PDF files. All you have to do is upload the files and let FormX take care of the rest. The extracted data is available as JSON or CSV file that can be sent to other systems for further uses.
For example, we want to extract the data from Coyote Bar & Grill’s receipts stored in a PDF file to analyze consumer behaviors. You can use the pre-trained receipt template of FormX.
To test the result, you can upload it to our portal and scroll down to the JSON Output section as shown below. As you can see, FormX has extracted data such as the total amount, date, time, and the price, name and amount of each product.
Let’s demonstrate with another receipt from MUJI. Although the layouts of MUJI’s and Coyote Bar & Grill’s receipts are very different, FormX can still extract the data with high precision.
The specific information about each product and the receipt has been extracted and organized by FormX within a few seconds as shown below.
Check out our use cases to see how businesses have benefited from incorporating FormX into their workflow.
If you have other types of PDF files from which you wish to extract data, you can also collect some samples of documents and use them to train the software. Thereafter, the only manual process would be to test and verify the extracted data from your PDF files.
Need to automate your data entry process and extract data from images or PDF files with ease? Talk to us about your workflow and needs for us to maximize your productivity.