Infrrd AI Enabled Intelligent Document Processing for Complex Documents

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 learn able by the machine. Mentioned below are the ways that, we at Infrrd, employ in preparing our data. 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 error-r...

The reality is that Banks exchange a lot of information with other companies that they have no control over. They get annual reports from customers, they need to validate payslips for processing loans and there is no global standard for that. A lot of time they need to process collateral documents which comes in thousands of variations from a growing list of hundreds of providers. This can be possibly due to the unprecedented challenges faced by banks: from in-branch applications for accounts to cheques, loans, monthly statements, and other services, much of their daily activities remains mired in paper and manual processes. Also, Most banks have switched to online applications where they control the entry of the data. The challenge is for the documents that come from other companies that they do not control But, what’s the problem with papers? In today’s fast-paced consumer culture, delays become a liability to any industry that provides customer service. Cheques and forms have ...

Thanks to its name and all the hype around Big Data, it gives a sense that Big Data is meant for big companies with a huge amount of data. It is generally believed that Big Data has nothing to do with small businesses that generate small chunks of data. True, Big data is changing the game for a lot of big businesses, but as an SMB owner, have you looked at how it can impact your  business ? Read on to find out how Big Data can work for ‘big enough’ data for Small-Medium Business. The answer might surprise you… THE ONE MINUTE ‘YOU NEED BIG DATA OR NOT’ TEST: Here’s a quick test for you to figure out if your business can benefit from Big Data Analytics: Does your business use computers to store business data? Do you exchange information with your partners, customers or employees using electronic media like documents, emails, etc? Do your employees, users or customers talk about your products or services on social media? If you are running a Small to Medium business in 20...

Intelligent Data Capture  Infrrd’s current wave of technologies has tested a multilayered extraction model successfully that employs a suite of advanced  AI-technologies  to streamline the operational process. Infrrd’s  Intelligent Data Capture (IDC)  implementation can help and make a significant impact on operations and workflows with near-perfect accuracy. It’s prolific and definitely the next big thing that’s gaining major traction in the enterprise environment. But this is just a tip of the iceberg. Adapting IDC provides a single point of access for all of your business information. Despite numerous advantages, decision makers still think twice about why should one invest in IDC? If you’re one among them  contact us  for a  free demo  today and get your questions answered about IDC or business-specific applications. Originally published at  infrrd.ai

Over the past 15 years, I have had the chance to work with many  OCR tools  and one thing I can say with certainty is that the text extraction quality of these tools has steadily improved with ongoing improvements in artificial intelligence and machine learning OCR techniques. More than ever businesses are trying to derive useful insights and meaning from scanned images and documents. For example, banks are wanting to extract intelligence such as parties involved and contract expiry dates from scanned contracts, insurance companies are wanting to detect fraudulent receipts submitted during the claims process and many more. Use cases like these require unstructured text be converted into structured meaningful data during OCR or post OCR. OCR tools inherently lack the intelligence to parse or understand extracted text beyond just extracting it. To assign meaning and structure to the content, another system needs to process the extracted text and extract entities and entity...