Beyond Data Extraction: How to Turn Messy Document Data Into Structured Tables?

Nobody ever says no to a table. If you work with numbers and data, you know how comforting a well-structured table can be. It’s the same information, but somehow, when presented in a table, everything just makes sense. Now, imagine how much time you and your team could saved if the data you need were already formatted neatly in front of you.

So far, most technology can extract data from existing tables. But what if the original document doesn’t come in traditional tabular formats with well-defined rows and columns? That’s where things get interesting.

Now, you can extract both tabular and non-tabular data and transform it into custom, structured tables—no Excel formulas, no coding required. Sounds too good to be true? Believe it. And the best part? It maintains high accuracy without compromising data quality.

NLP Supremacy for Table Data Extraction: When Machine Learning Falls Short

Machine Learning (ML) has long powered data extraction, handling structured and semi-structured documents with pattern recognition. But when data gets unpredictable, ML struggles, stumbling over new formats and requiring constant retraining. That’s when NLP, natural language processing, enters the scene.

Unlike ML, NLP doesn’t just read—it understands context. It breaks free from rigid structures, effortlessly extracting data from complex and unstructured documents. No retraining. No predefined formats. Just smarter, faster, and more accurate extraction. The following is a quick differentiation table between ML and NLP. 

Did You Know? Over 70% of the documents in the world are unstructured. 

How to Find the Best Table Extraction Tool for Complex Documents

Finding a data extraction tool is easy. Finding one that accurately extracts data into tables? That’s the real challenge. Here’s what to look for when choosing the best tool for table extraction, especially for complex documents.

1. ML, AI & NLP-Based Systems

For years, OCR (Optical Character Recognition) was the go-to technology for data extraction. But here’s the truth: OCR alone isn’t enough. It simply converts documents into a string of text based on predefined key-value pairs, limiting its accuracy and flexibility.

A step up from OCR is ML & AI-powered OCR engines, which add a layer of intelligence to the extraction process. However, the most advanced solution is Intelligent Document Processing (IDP)—an AI-driven system that extracts data from even the most complex documents.

But here’s where things get tricky: Extracting messy, unstructured data into clear, structured tables is beyond the scope of ML-based OCRs. If you’re dealing with complex documents, look for tools with Natural Language Processing (NLP) capabilities.

2. Trained on Real-World Documents

How do you ensure the accuracy of extracted tables? It’s not just about choosing a tool—it’s about choosing a well-trained tool. The best extraction tools are trained on millions of real-world, industry-specific documents to refine their accuracy. When evaluating vendors, make sure their system has been rigorously trained for your industry.

3. Template-Free Table Data Extraction

You want data in tables—we get it. But what if your documents aren’t structured in a fixed format? Many traditional tools struggle with this, forcing you to use predefined templates. To avoid this limitation, ask your vendor if their tool supports template-free data extraction. This ensures it can handle any document format, no matter how unstructured.

4. Custom Rule Configuration

Table extraction isn’t just about pulling data—it’s about how you structure it.

• Do you need specific rows and columns?
• Should certain columns be merged or separated?
• Do you want to automatically calculate extracted values?

Depending on your industry, you may need customizable extraction rules. Ensure your tool supports custom table configurations to meet your exact needs.

5. Industry-Specific Expertise

Accuracy depends on how well a platform understands your industry. A generic tool won’t cut it if it doesn’t recognize critical data points, industry-specific jargon, and compliance requirements. When choosing a vendor, look for a dedicated R&D team that specializes in your industry. A tool trained in your field will always deliver better, more accurate extractions.

From Theory to Reality: Infrrd’s NLP-Based Table Data Extraction Hits the Spot!

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For years, converting non-tabular data into structured tables was just a theoretical concept. But through our work across industries, we realized that businesses today need more than just data extraction—they need data formatted to fit their industry’s unique requirements.

When clients first approached us with this challenge, no vendor offered a solution. Even today, AI vendors hardly match our level of accuracy and industry-specific customization in table extraction. With Infrrd’s custom AI-powered extraction model, businesses can automatically transform unstructured data into tailored tables—no manual intervention required.

For example, let’s look at insurance documents. Here’s a sneak peek into how our platform seamlessly converts complex, unstructured data into structured tables with precision.

Infrrd Table Data Extraction Step-By-Step

Step 1: Document Upload

We start by uploading multi-policy documents that present data in an implied table format. We call it an implied table because the data is not structured in a traditional tabular format with rows and columns. Next, we upload this document to Infrrd’s insurance document extraction platform.

Step 2: Magic Table Rule Configuration for Tabular Data Extraction

Customize the rows and columns of the data you want to extract into a structured tabular format. In many other platforms, this step is considered a separate customization process—one that is often costly and time-consuming, sometimes taking days or even weeks, depending on the document’s complexity. However, here we’ll show you how it’s done within the platform, without any coding or complex customizations.

Click on the Magic Table Configurator option, and from there, you can start by defining the table details, such as the table name, column headers (e.g., policy numbers, insured names, policy types, effective dates, etc.).

Step 3: Alternate Titles

Under the Magic Configuration section, there is an option to add alternate names for tables, fields, or values. This improves extraction accuracy and allows the system to go beyond fixed templates by understanding various field names.

For instance, different companies use different terms for the same value. Take policy number as an example—some companies call it "policy," while others call it "policy number." By adding alternate names, you enhance extraction accuracy.

Step 4: Custom Hints

Additionally, you can provide custom hints to improve the system’s accuracy and reliability. For example, you can:

• Define the expected structure of a policy number.
• Specify date formatting preferences (e.g., converting dates written in words to a standardized numerical format).

The key idea is that you can customize and standardize extracted data to fit your business needs.

Step 5: Contextual Table Data Extraction

Click Try Now, and within seconds, the extracted data appears in a well-structured table with the rows and columns you specified.

For business professionals—especially data entry teams—this feature is a game-changer. It not only extracts data but also organizes messy, unstructured, and unpredictable information into clean, well-structured tables, without relying on IT support or Excel sheets. Best of all, users can configure everything themselves in just a few seconds, without writing a single line of code.

Step 6: Post-Extraction Table Data Customizations

What if you need to add or remove rows or columns after extraction? That’s possible too! You can easily update your table with new parameters, directly within the system—again, without writing a single line of code.

The key idea is maximum flexibility—you can extract and organize data in a way that aligns perfectly with your business requirements. Voilà! It’s that simple.

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