HyperScience Reviews

My projects are from different PDFs. I use a KFI8. First, we create an email using email extraction. After that, we email or scan the document and print a standby so we have their input in different formats: email, scan, and print. We then put it into HyperScience, which provides the JSON output. Now we can automate using any application, such as RPA Automation Anywhere, which gives you the RPA UI Path. 

Valuable features include tools like IQ Bot and the ability to extract handwritten documents with 93-95 per cent accuracy, which can be improved using ML technology. 

This is an OCR solution. We were testing it for one of our existing processes. We were checking its capabilities on how it can provide streamlined services for data entry processes and automate the step that was done manually. 

It was on the cloud, and we had a test instance given by HyperScience to perform all these things.

For our use case, it provided a complete workflow. It reduced our existing touch time by 80%.

From the point of view of technology, it is one of the leading platforms right now in the OCR world. It provides the best accuracy for handwritten forms, which is a struggle in the industry. You can take processes with a lot of manual work and streamline them through this tool. So, there are multiple benefits attached to HyperScience being inducted into any organization.

We use HyperScience for testing purposes, document processing, and automation. We tested with both printed and handwritten documents that were related to the medical field. To get better results, we had to test the documents written in cursive and configure the maximum number of documents in HyperScience. If you want better than expected results, you must first configure more documents for that particular vendor.

We're automating invoices and some handwritten filled-out documents. We can automate with accuracy, and even though we used HyperScience initially for selling purposes and as a comparison tool, we're now getting these results with HyperScience. 

We use this product to help our internal teams automate in areas such as claims automation using intelligent document processing. It deals with handwritten documentation. We are partners with HyperScience and I'm a data science manager. 

The product performs well on handwritten text and on checkboxes with regard to data extraction. It also has some algorithms that can detect a document template even if it is rotated in different directions and if the image has a lot of distortions. It has a great feature called visual template matching, which identifies the template and extracts the data.

We use HyperScience for extractions.

What I liked more about HyperScience was the quality of the OCR it is a lot better compared to Google.

There is a feature called Visual Template Projection which tries to identify the template of the document that is submitted regardless of the orientation of the document, whether it's visually damaged, or in very bad shape. It will try to match the template.

We are using HyperScience at an enterprise level for all of our document extraction.

We work in the financial services domain. As a result, a lot of our documentation comes through in hard copies as paperwork, such as application or enrollment forms. These documents are consumed by our business team in several different ways and to facilitate this, we use HyperScience. It is used for all of our papers that are coming in, they are scanned in digitally at our scanning facility.

To facilitate further automation or integration with some of the existing systems that we have, we use HyperScience to extract data off of these forms and then pass it through other applications or other tools that we use for automation.

A lot of our interaction with our clients has been through paper documents because of the nature of the business. When we were in process of digitizing our documents, it was not easy to onboard everyone to send in copies electronically. A lot of people were not very tech-savvy. They were still comfortable in sending things through paper. We wanted to keep that option for them without causing much of a disruption in the way we have our customer engagement.

Having a tool like HyperScience which facilitates the process of converting digital copies of the scanned documents and provides us with the extracted data, has helped us to facilitate further automation. Once we have the extracted data, there could be several different ways through which we can consume that data.

We use it for all of our structured, unstructured, and handwritten documents.

As far as the product goes, I really like the product. It’s clear why it is the market leader.

We have seen pretty good accuracy.

There are all different kinds of use cases that we get out of it - especially for its core capabilities. We are experiencing pretty good results with the core capabilities overall.