Frequently Asked Questions
AURA - DCap
faq

    • There are no limitations for the pdfs as long as the pdfs are in the same format in one model.
    • Size of the image should be less than 4mb

  • Yes

  • After the training is done, we can see the average accuracy of the model including the individual %s of the fields.

  • Yes, this falls under the managed service type where we extract the data and perform end- end operation for the customer.

  • Yes

  • No... As many templates are present, those many models/projects need to be there.

  • Based on the client's requirement, we can provide the error log link to the customer. Like an e-mail notification, or a text message with the detailed info of error files.

  • No, one can use this tool on a tenant basis. All the extracted data is available on our cloud.

  • Yes

  • Yes

  • Please contact us >> link to contact us takes to contact us page

  • AURA DCap is customized to the client’s requirements; the data extraction process is the same for all the different formats of docs. But the add-ons like Invoice matching, signature detection, Checkbox detection, documents with pictures detection varies from client to client. We have currently two versions based on the client’s requirements

  • A simple JSON message to data from your existing RPA system can be fed as input to our AURA DCap and the output can be written to customers’ existing database and the third-party application for data storage.

  • Processing time takes up to 5 minutes.

  • 500 is the limit.

  • There is no limitation, more people may use it in parallel.

  • We recommend that you use five manually labelled forms of the same type to get started when training a new model and add more labelled data as needed to improve the model accuracy.

  • No

  • File Size: < 4 MB.

    Number of Pages: < 50

    Minimum Image Size: 50 x 50 Maximum Image Size: 4200 x 4200

  • No

  • Please contact us >> link to contact us takes to contact us page

  • English, Chinese (Simplified), Dutch, French, German, Italian, Portuguese, Spanish.

  • JSON, XML

  • Any web browser.

  • Please contact us >> link to contact us takes to contact us page

  • Printed and handwritten forms, PDFs, and images.

  • Training % depends on the format of the pdfs / positions of the fields you're interested in. The same kind of pdfs should be uploaded in a particular model/project for the % to increase

  • Due to some reasons, like a different format than the trained documents, or quality enhancement issues, some docs could not get processed. Those docs sit in our error queue. The support user gets notified when this happens, and the user can retrain documents and successfully process them.

  • This we can say based on the complexity of the documents, some are easy to understand and some need extra processing if they have minute details that are hard to capture. We can say a minimum of 4 weeks to maximum 6 weeks.

  • 5 PDF

  • AURA DCap automates the data extraction process by up to 90% with AURA . Along with Data, extraction AURA DCap performs further validations like-
    • Checking the extracted data to master data.
    • Identifying the checkboxes are a difficult task coming to the docs, AURA DCap uses AURA and Machine learning techniques which enhance the quality of the document allowing even the minor details to be captured with ease.

  • AURA DCap is well trained to understand both the sorted data and unsorted data. For example, if a big merged pdf with 100 docs is the requirement from the customer, AURA DCap can sort the unsorted merged pdf by itself and reads the doc as a human.

     

  • Based on the customer requirement, we can save the documents in our database until the docs are processed. Some customers drop the docs in their SharePoint and our team can upload the docs directly from the SharePoint. So based on the customer's use case, we can save the docs in our database.

  • East us 2

  • All the trained models get stored in azure blob storage. As long as the models are present in the blob storage, we can recover them.

  • AURA  DCap can fit into any organization across all verticals.

  • AURA DCap is an automated tool that extracts the required data from the documents by using Artificial Intelligence. Manual document processing is a major cost driver in organizations. Our deep learning technology automates complex document processing tasks and AURA DCap is an advanced version of OCR.

  • A general resource with a non-technical background can understand the AURA DCap UI and can process the documents. It saves a lot of money and 90% of manual efforts are replaced by automation.