Data extraction in 15 minutes: Step by step guide

An RPA <> AP automation project needs an invoice data capture process that keeps up with the robots: reliable, accurate, scalable, layout-independent, rapid to implement, and easy to integrate. Traditional OCR-based systems just will not fit this bill.

Rossum’s cognitive platform is able to capture invoice information without any template setup, such as invoice table OCR, and UiPath robots bring the ease of integration to the extreme. In this step-by-step guide, we provide the source code that you will need to automate invoice processing in 15 minutes.

Data extraction in 15 minutes

How table OCR solutions work

Extracting data using a table OCR is a straightforward process — if you have the right solution at your fingertips. Otherwise, you may be left with a long, endless amount of manual data entry and processing. 

Data is one of the most important currencies in the world today, but invoices, receipts, bills, and purchase orders have existed for centuries. From personal budgets to corporate expense reports, some things simply won’t change. However, the way people use data is always changing. Some people still prefer paper records; others keep everything digital. 

No matter the format, converting this data into an easily digestible, searchable, and archivable format is an important part of data reconciliation and compliance. Converting that data at scale requires an artificial intelligence-based OCR capable of ingesting tables regardless of their format, processing them, and converting them into a readable, searchable format of your choosing. 

The importance of using table OCR solutions in business

In business, having the right data could be the difference between sustained success and failure. Without the right information at your fingertips, you will be unable to keep your competitors. With the correct insights, analysis, and processes, you can automate some business operations, achieve greater scalability, and have more information to train your employees. Data is king. 

Solving the key problems in table OCR data extraction

The first problem we’re faced with so much unstructured data is this: how do we capture the data intelligently for future use? Highly capable OCRs are available to extract and process data from documents at scale quickly. But once it’s scanned, processed, and extracted, data needs to be in a structured format to be useful in systems or other processes. 

Most business data is now stored in unstructured data types like PDF documents and tables, images, and other formats that are difficult for machines to read data from. The overall goal of an OCR solution is to convert this unstructured information into usable data by digitally scanning the document and then extracting and capturing the data within that document. 

This data extraction process can enable you to automate entire business processes, give you cloud access to documents, and result in a boost in productivity and team motivation. 

Images of tables or PDFs of tables that include important data have, historically, been incredibly difficult for machines to understand. In an ideal world, an image of a table would be scanned into the system. The system would then identify the rows and columns and build them to fill the appropriate cells with the appropriate data. This digital table would then be exported in a usable format like an Excel spreadsheet. 

This is ideal, but it very rarely happens. This leaves you wondering how to convert images to text in Excel. Even expensive OCR solutions that promise table data capture features will often fall short. This means that your employees will have to regularly come in and correct all the errors that the machine made. Table OCR solutions are a breeze for advanced AI-based technologies like Rossum. 

For many reasons, Rossum is the best PDF and image-to-Excel converter anywhere. One strength of Rossum is that table OCR occurs within its easy-to-use validation interface, which allows users to quickly and easily refine the data that the OCR has captured in just a few clicks. After that, the table can be exported to the format of your choice for later analysis, search, or archival. 

How to: extract a table from an image

The goal sounds simple – “extract table from image” – but experts have been trying for years to build a solution that could flawlessly capture data from an image of a table and export it to a structured tabular format like Excel. 

Computers struggle with commands like “convert image to Excel table” that human employees do not feel. This is why, for so many years, data entry has been handled by human employees. They have been responsible for completing each task on the checklist, including “extract data from the image to Excel.” This is the most powerful form of data capture. 

One of the primary challenges of using OCR for tables is their struggle to detect a table. Systems like this look for patterns to know what they are looking at. The problem is a huge amount of variability in how tables are organized in images and PDF files, making it difficult for machines to learn the patterns. 

To solve this problem, Rossum was created, giving organizations the ability to effectively capture data from images of tables easily and with nearly 100% accuracy. 

Rossum leans heavily on machine learning and artificial intelligence to achieve this. Powered by neural networks that mimic how our brains process information, Rossum has a uniquely powerful ability to capture data from unstructured formats. 

This can be very useful in invoice processing in the accounts payable department. Often, there are line items and tables on invoice documents. If you have a system that recognizes these tables within the invoices, you can capture all the data. This kind of automatic data capture opens opportunities to automate your business processes and creates new ways to grow your business. 

How OCRs use deep learning for table structure recognition

At the heart of innovations like Rossum is deep learning. To achieve a capable table OCR online solution, artificial intelligence is required. Deep learning is a part of the disciplines of both machine learning and the broader umbrella term of artificial intelligence.

To successfully build an extracted table from a scanned PDF Python program, a system with deep learning is required. Simply put, deep learning refers to the process of teaching computers to recognize patterns. Neural networks are the technology that powers deep learning and makes it possible.

Neural networks are designed to assist a computing system in being able to learn and store information similarly to the way a human brain stores information. In other words, neural networks allow a computer to “learn” things. Why does deep learning matter when we want to extract tables from scanned PDFs? 

The roadblock that comes up when attempts are made to scan images for tables of data is the variability of the way tables are displayed. It can be very difficult for a computer to be able to recognize patterns when there are so many different possibilities. 

This task requires the computer to use more abstract reasoning than is usually required from artificial intelligence systems. This is why systems like Rossum become smarter, more accurate, and ultimately more capable the more data they process and analyze. 

Is there a good table OCR API?

Yes! A well-built table OCR API should be able to scan documents and images and extract the table data from them accurately. Rossum’s API is specifically designed to be able to handle tables from any channel with accuracy. This is vital if you want to be able to use the data within those tables for automation purposes. 

Cognitive OCR platforms use AI to “read” documents like humans do and can operate much more quickly and efficiently. A good OCR API could handle large amounts of documents and still ensure that all data was received, captured, and exported correctly. 

Using an OCR solution as part of an overall automation strategy can end up freeing up time for your employees and teams so that they can focus on tasks that can grow your business. 

Automation also brings scalability. In the past, teams like accounts payable could end up holding back the company’s growth. These teams would be quickly overwhelmed by the uptick in the volume of orders and invoices to manage. This could lead to some team members leaving or mistakes being made, leading to further delays. 

With an automated system powered by a table OCR API, you can ensure that fluctuations in the order volume do not disrupt ordinary operations, and growth is much easier to accomplish. 

OCR table extraction: custom-built OCRs vs. free online tools

Over the years, several methods have arisen to achieve accurate OCR table extraction. Python is one of the most popular and easy-to-learn programming languages. 

Some companies have written table detection OpenCV Python programs. Others, like Tesseract OCR table recognition Python programs, are another category of rough-and-ready table extraction options. These are Python programs that utilize the Tesseract OCR engine for scanning. 

These custom solutions require in-house personnel for maintenance, configuration, tweaking, and troubleshooting, leading to expensive, time-consuming processes outside of a company’s area of focus.

Free OCR solutions are available online but are rife with issues. First and foremost, can you trust a sketchy, free website with your company’s sensitive information? And even then, is it worth the time and hassle involved in correcting the errors these free solutions always create? 

The limitation of these “extract table from image online” programs is that they are not completely built solutions. They lack the security features and full functionality required within a business setting. 

Using Rossum for simplified table OCR extraction at scale

As you can see, there’s no easy way to get table OCR performance without serious time commitments, custom coding, or trusting your data to sketchy free websites — except for one. 

Rossum is the key element in extracting data from tables into a usable format for future use. Rossum offers secure data extraction from unstructured documents, helping you organize and easily find the insights in the data you’re looking for at any scale. 

How Rossum extracts table data

Step 1: Login to Rossum

Step 2: Get started (or similar)

Step 3: Configure for table extraction

Step 4: Upload your documents

Step 5: Quick results

As you can see, Rossum can extract and tabulate all your information correctly and without errors, helping save time and energy. After processing, the data can be converted or processed into whichever format you’d prefer. 

OCR-to-table conversion: the Rossum differencce

Although you may find a table OCR-free solution, you may not be getting the quality you need, and you’ll get what you pay for. Most of these programs are highly error-prone and finicky and don’t properly secure your sensitive business data.

Rossum provides a high-quality OCR-to-table conversion as part of our intelligent document processing (IDP) solution. To unlock your business data and create new automation opportunities, go beyond simple OCR data capture and utilize a comprehensive IDP solution like Rossum. 

A Rossum configuration makes it simple to convert large numbers of documents into more useful forms of data. With Rossum, the OCR table to Excel process is simple, saving you more time to focus on growing your business.

Automate your table data extraction

Extracting data from tables is no easy task, especially when it comes to complex line items with nested values that are multiple pages long. No human should be stuck spending their days entering the data manually. Make the change today and move to an intelligent automated approach
that will free up both your time and resources.