So let’s dive in on how to extract data from tables, then clean and transform that data so it can be consumed by an automation system or an ML platform.
That way, you’ll getwhat you want—the speed and cost-savings of automation—and what you need—the extracted gold trapped in your tables.
What is a table?
I know. Sounds silly, right? That is until you consider that there’s no standard for what makes a table...no universal definition.
Tables are an intuitive and universal way of presenting large sets of data, findings, and information.
What’s more? A table is more than its data.
Tables contain a variety of data and information (e.g. words, digits, formulas, or images) and are embedded in a variety of document types (e.g. plain text, image, handwritten, or web pages).
But wait, there’s more!
We can’t forget relationships and context.
And there’s where tables are unique.
A table displays multi-dimensional information using a two-dimensional, linear format. It’s a set of data and data context presented in a non-standard format.
Because of this unique feature—displaying not only sets of information but their relationships and context—tables present a challenge for data extraction. And, that challenge expands when you consider there’s no universal format for a table: You might have three suppliers send you three different tables in three different formats...all trying to measure and show the same thing.
The Challenges of Tables
So let’s call out all the challenges tables present. That way, we can see how they may be overcome. That way we can see if we CAN get what we want, as well as what we need.
Challenge 1:No standard structural layouts or visual relationships
The structure of a table is determined by the structure and the relationships of its cells.
Tables capture multi-dimensional information using a two-dimensional, linear format. There is no standard table layout. For example:
How lines are used
How formatted (e.g., bold or italicized) text is used
Header location: Headers can be in two places—the top row or the first column
Use of borders: A table has or may not have a border, which makes it hard to locate
Variation in styles: separators for cells, rows, and columns
Nested tables: one table inside another
Cells spanning across columns and rows show hierarchical grouping of data
Word wrap and column merge allows content to span across multiple lines and cells
Multi-page tables that are used for long data displays. In some cases, headers repeat
Tables that float and have text around them
Challenge 2: Data visualization for humans…only
Formatting is for visual consumption by humans, not technology. Often, table design is poor. Consider the header rows that aren’t explicit, but rather implied based on the table or the supporting text.
Challenge 3: Cell content that uses various formats (letters, numbers, symbols, etc)
When cell values are presented using different syntactic representation patterns—like symbols, images, text, abbreviations, or mathematical notations—extraction requires knowledge of all possible presentation patterns.
Challenge 4: Multiple languages in cell content
The table and its cells may use different languages or domain-specific jargon.
Challenge 5: Cell content varies in density and formats
The content of the cells can be numbers or text. But what happens when cell content is dense, containing ambiguous, short chunks of text with the use of acronyms and abbreviations? To decode tables, text must be made more clear with abbreviations and acronyms fully defined.
Challenge 6:Document types may vary
A document and table can be in a PDF, text, image, HTML, or another format. Some formats are more challenging than others. For example, the PDF format has no internal representation of a table structure, which makes it difficult to extract tables for analysis.
How to Get What You Want...AND What You Need
This list may not be all-inclusive, but it’s a good start. When it comes to data extraction, tables are tough.
When you want to free your workforce to focus on more value-based action (and when DON’T you want that?), you want to be able to enable the automation and ML processes to take the action instead.
To do this takes just four steps.
Once you understand the challenges tables present...and how to overcome them, getting your prescription filled is a whole lot better than standing online at the Chelsea drugstore with that Mr. Jimmy character.
Want to get what you want AND what you need? Let's Chat!
Start a conversation to get what you want and learn a lot more about what it takes to overcome tabular data extraction challenges.
How can I try Infrrd before I commit to a full deployment?
Sure. The first step is to schedule a guided demo where you get to jump into the thick of it. After you explore our solution you can try a proof of concept. When you're ready, you can deploy the system to one use case. Then more use cases. Then across your enterprise.
Glad you asked. Our data extraction process runs on servers. We have found performance and accuracy decline when running on a desktop or mobile device. (Remember Infrrd is running a powerful AI stack).
In a fast-paced world filled with never-ending rivers of documents and data, organizations continuously need smarter ways to work. Teams need flexible solutions that enable them to work faster while delivering higher levels of reliable accuracy than ever before. At Infrrd, we empower teams with Intelligent Document Processing Solutions for Intelligent Work™.