What Mick Jagger Taught Me about Data Extraction from Tables

-

Data Extraction From Complex Documents

How accurate is your dataextraction from tables?

Let’s face it. Extracting information from tables to feed an automation process is as complex as it is difficult.“You can’t always get what you want” is further validated when we recognize that automating the information extraction process from tables is a challenge few have fully conquered.

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.

             READ MORE...


                                          
Location: United States

Comments

Post a Comment

Popular posts from this blog

Eliminate your OCR and Manual Data Entry Bottleneck

-
Image
Intelligent document processing This new  AI-driven  approach to data processing can do far more than OCR.  What is IDP?  Why is it different? When is it better? Is it right for me? This eBook is an executive guide to Intelligent Data Processing , a new AI-driven solution that can  unlock the data  you need to drive intelligent automation and improve business processes.  Get an overview of IDP, explained in simple terms. Learn what it is, why it’s  fundamentally different from OCR , and how it can be applied to remove bottlenecks in your business. Includes three examples of  typical use cases .  Download The eBook To know More 😁 https://hubs.ly/H0rq_Lk0   
Read more

How to Prepare Data For OCR Learning

-
Image
Data analysis without data preparation it is a myth. Unless we feed the right data in a proper format, Machine Learning algorithms won’t be able to solve our problem. If we give one wrong input then we end up where we started. So it’s very important to understand what data preparation is and how one can do it. Data, in its original form, may have a lot of missing pieces or disarrangement. Through data processing, one can modify this raw information from a specific database to a format which is understandable and learnable by the machine. Mentioned below are the ways that, we at Infrrd , employ in preparing our data. 1. Data selection: It is necessary first to identify the type of data we are going to be working with. One has to keep in mind whether the available data will be able to address an existing problem or not. We keep certain factors in consideration before selecting the data: Data should not be of low quality: Low-quality input= low-quality output. Dataset is not err
Read more