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.
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.
Lead Analyst at a financial services firm with 1,001-5,000 employees
Real User
2022-03-23T16:48:18Z
Mar 23, 2022
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.
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.
HyperScience is a cloud-based input-to-output platform that automates document-based workflows and extracts data for analysis from both physical and digital documents.
The HyperScience platform simply integrates with your current business systems thanks to an open API, an intuitive UI, and pre-trained models, resulting in a totally automated digital workflow.
The classic scanning paradigm has been reimagined by HyperScience; it has replaced the need for human verification with intelligent...
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.
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.
We use it for all of our structured, unstructured, and handwritten documents.
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.
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.