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Extracting data from different types of documents is one of the big challenges and a monotonous time-consuming activity, as we cannot pull the data from unstructured PDF files, forms etc. Plus we are not able to process multiple documents at a time.

This solution is useful for extracting the data from multiple PDF files, invoices, accord forms, scanned images and hand-written documents. It is able to identify the checkboxes as well, it can extract the data from any type of structured, semi-structured or unstructured document by using document understanding techniques​.

This solution helps with extracting the data from multiple PDF files, invoices, accord forms, scanned images and hand-written documents.

**Acord Form & other unique document types data extraction by using Intelligent Document Understanding functionality.**

  

Extracting varied data from different types of documents is a key challenge and a monotonous time-consuming activity for insurers, as the pattern and format of such documents appear unique which is a cumbersome task of not being able to extract the data from these documents quickly.  The documents that need to be processed are either structured, unstructured, or semi-structured Acord forms, schedules, loss runs and so on.

  

Since the amount of data to be extracted from these set of documents is varied, it becomes a difficult task to understand the importance of data to be extracted as well as to process multiple documents in a single go that are typically received as email attachments during a new business submission or claims intake process.

  

This solution is useful for extracting these unique data from multiple document types and formats that may be printed, scanned images or hand-written. By using the intelligent document understanding functionality, it becomes simpler to identify and understand the data elements and data types that need to be extracted from different intake document types.

Extract the data from unique structured, semi-structured and unstructured Acord forms, schedules, loss runs etc. that are printed – PDF files or scanned images and hand-written documents.

**Acord Form & other unique document types data extraction by using Intelligent Document Understanding functionality.**

  

Extracting varied data from different types of documents is a key challenge and a monotonous time-consuming activity for insurers, as the pattern and format of such documents appear unique, which is a cumbersome task of not being able to extract the data from these documents quickly.  The documents that need to be processed are either structured, unstructured, and semi-structured Acord forms, schedules, loss runs and so on.

  

Since the amount of data to be extracted from these set of documents is varied, it becomes a difficult task to understand the importance of data to be extracted as well as to process multiple documents in a single go that are typically received as email attachments during a new business submission or claims intake process.

  

This solution is useful for extracting these unique data from multiple document types and formats that may be printed, scanned images or hand-written. By using the intelligent document understanding functionality, it becomes simpler to identify and understand the data elements and data types that need to be extracted from different intake document types.

For segregating files, train each type of documents into keyword classifiers from document understanding techniques. Based on the keyword classification the documents will be place into a designated folder. The documents which are not trained will be placed in unknown folders.

Segregating the files from different types of documents in one folder is a big challenge and a time-consuming activity as we cannot segregate the set of files in one single folder.

By using the document understanding capability, the respective unique new business and claims intake document types are segregated and classified intelligently using a keyword classifier. Each time a new intake document is received the tool is capable of learning the type of document using the key word classification and is then segregated and stored in the designated folder.

This solution helps in segregating different document types by classifying them into their respective categories and storing them into a designated folder.

Software Engineer

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This packahe is useful for extracting data from documents with different formats, by using machine learning models present in AI Center, combine with Document Understanding. It will fetch the required values such as invoice number, amount, date etc.The main advantage of this solution is that it works even though there are different document formats.

This solution extracts data from invoices with different formats. It uses a combination of ML models present in AI Center and Document Understanding.

This package is useful for extracting data from documents with different formats, by using Machine Learning Models present in AI Center, combined with Document Understanding.

  

The solution can fetch the required values such as invoice number, amount, date. It can take advantage of existing Machine Learning Models from UiPath AI Center and extract invoice data even when the input file is with different document formats.

  

The component is useful for checking the feasibility of template data extraction from a big set of vendor/supplier invoices using UiPath DU-Invoice ML offering. It can process invoices with maximum 2 pages per invoice and file size of max 4MB with enterprise trail license.

This solution uses a combination of ML models present in AI Center and Document Understanding to extract information from invoices with different formats.