Intelligent Data Capture With Artificial Intelligence

-
Infrrd came up with a unique approach. Our ‘Intelligent Data Capture’ (IDC) encapsulates advanced AI-enabled capabilities such as Machine Learning and NLP to trawl through reams of documents to detect, analyze, and classify tables. But it doesn’t end there. Next, the data is captured in digital format, which routes data throughout the business environment for future purposes.
Infrrd’s machine learning capability can:
  1. Identify tables in the document (their outer boundaries)
  2. Segment the table to recognize and detect the inner boundaries of the table (i.e. the rows, columns, and individual table-cells).
  3. Classify tables into different types (e.g. complex, long, folded, simple) using layout features.
  4. Extract table content
Also, the Natural Language Processing (NLP) techniques help interpret table content (such as table titles, footnotes and non-table prose discussing a table), as well as understand both cell content and its relationship to the table content. Integrating this platform into your business will reduce the time and risks of errors associated with manually rekeying datasets from tables, which will ultimately impact your service delivery in a positive way.
At times, due to the complexity and diversity of documents, implementing IDC technology can be challenging. However, consulting with the right partner will ensure you balance your data capture needs to get you the functionalities you need to maximize business outcomes. Now, the question is ‘where are we?’.
Get in touch with our experts for consultation or a free demo to understand more about Intelligent Data Capture and its abilities.

Comments

Post a Comment

Popular posts from this blog

What Mick Jagger Taught Me about Data Extraction from Tables

-
Image
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 ...                                           
Read more

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