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Top 8 Intelligent Document Processing (IDP) Tools

ABBYY FlexiCaptureWorkFusionIQ BotIBM DatacapHyperScienceGrooperEphesoft TransactOpenText Intelligent Capture
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    We have found the solution to be stable so far. Time-saving with increased levels of accuracy.
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    The most valuable features are the Excel activities. It has a number of Excel activities that are quite reliable.We recommend different solutions based on our customers' needs, and WorkFusion is what we offer for those who need a premium solution.
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    The initial setup was satisfactory.I personally feel that the c-extraction feature is valuable. After training it for a particular document, we can see the extractions there. Other than that, it has other features like Python scripting in-built, and these are the features that I feel stand out.
  5. While we are doing indexing, we tag the document type. It's programmed inside of Datacap to automatically detect the document based on a given template. It auto-indexes that document, which means that it automatically tags the correct document type to the scanned document.
  6. I like that compared to other tools, HyperScience works best with handwritten documents.
  7. Grooper processes difficult sorts of data and unstructured or semi-structured content very well. It's probably one of the better solutions I've seen compared to other solutions I've seen out there. It does a lot more things like segmentation extraction. It does it a lot better. Grooper has more focus on these types of freeform documents where other solutions are very generic and this is a little more elaborate in what they've done. I think they take it to the next level of extracting freeform data.
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  9. I like the inject feature. With desktop injects, we're doing all these things and extracting these things. Its stable, scalable, and technical support is good.
  10. Being able to have accurate extraction is most valuable. It has machine learning and other tools to make the extraction more accurate. We use it to capture documents coming through our product and mobile channels and make them available for our business to access and email. We use it to automate the email workflow in our applications, pull the documents, and email them to the users.
How does intelligent document processing work?

This kind of document processing not only scans a document but also makes it intelligible, extracting the content, the layout, and even the images. Intelligent document processing is a form of intelligent process automation (IPA) that combines technologies such as natural language processing (NLP) and machine learning (ML) to automate tasks in document processing.

IDP helps organizations that receive a high volume of unstructured and semi-structured data. Unstructured documents like sales orders, invoices, and such, usually cannot be processed using traditional automation software.

Intelligent document processing involves several steps:

  1. Before processing: IDP solutions apply techniques to improve the quality of documents. These include noise reduction, binarization, and de-skewing.
  2. Processing images: IDP uses computer vision technology to identify text, graphs, and pictures. Computer vision is a subfield of artificial intelligence that aims to help computers observe and understand image data.
  3. Extracting and classifying data: At this stage, the IDP solution uses natural language processing (NLP) to understand and give context to human language. This means that after IDP extracts the text, NLP is used to classify and make sense of the documents. It categorizes the document’s components into text and images. For example, processing a loan application can include ID documents, tax forms, bank statements, etc. An IDP tool parses out these different components, categorizing them for the next stage. This is one of the key deliverables of an IDP tool - detecting important information and extracting it for processing. It does this by using libraries of pre-trained extraction models.
  4. Data validation: IDP tools leverage pre-configured databases to validate the data they extract from documents. At this stage, problematic data is delivered to human agents for review.
  5. Integration: The last stage of the IDP process involves integrating the data into the organization’s existing systems.
Why is intelligent document processing important?

IDP helps organizations combine the data from paper documents with the massive amounts of digital data available. It provides organizations with visibility and a comprehensive evaluation of the data. IDP also facilitates end-to-end process automation. Since it can be integrated into any platform, it helps streamline processes. Additionally, this technology helps other AI analytics and machine learning processes, which require structured data to train and operate.

How is IDP different from OCR?

OCR (optical character recognition) is a technology that captures and converts an image into machine-readable text. This process is useful for digitizing documents. However, it doesn’t extract context from the content or interpret the data.

Here is where IDP comes in. IDP uses OCR technology to convert an image of the text into readable text. Next, it uses machine learning and deep learning to classify, interpret, and process the data. Additionally, the data can automatically integrate existing workflows for further processing.

IDP goes beyond OCR capabilities, processing a wide range of paper and digital documents, unstructured and structured data, handwritten text, etc. IDP recognizes and captures the content and extracts the context from the content.

As an example, processing a simple check requires several layers of processing:

  • Recognizing the type of check and the bank that issued it.
  • Capturing the digital text printed by the bank and the text handwritten by the user.
  • Recognizing the amount in numbers and text.
Benefits of Intelligent Document Processing Tools

Intelligent document processing identifies and extracts data from documents, processing it so it can be ready for analysis. Some benefits of intelligent document processing tools include:

  • Increased efficiency: Manual document processing is prone to error, and costly in both human hours and paper. The automation feature of IDP reduces errors and reduces the cost and time involved in manual document processing.
  • Improved compliance and security: IDP produces increased accuracy in document verification, enhancing compliance and management of sensitive documents. IDP takes the humans out of handling sensitive data in personally identifiable information documents. IDP can streamline the accuracy of regulatory reports.
  • Better data quality: IDP provides organizations with easy, reliable, and accurate access to the data contained in unstructured documents like emails, texts, PDFs, and scanned documents. By verifying, analyzing, and processing the data, IDP improves the overall quality of the data for the organization.
Features of Intelligent Document Processing Tools

Intelligent document processing converts unstructured data from documents into structured data. It classifies, extracts, and automates the processing of data from documents. There is an array of document processing solutions, offering a range of features. What should you focus on when trying to select an intelligent document processing tool?

  1. Look for a solution that automates the extracting of unstructured/semi-structured data. The solution should eliminate manual processes such as data entry.
  2. It should integrate with existing systems, including accounting and workflow processing.
  3. The solution should be able to decipher scribbles, handwriting, and difficult content.
  4. Some IDP solutions automate document creation by gathering data from distributed sources.
  5. It should help digitize paper documents, streaming them into machine-readable formats that can be processed down the line.
Find out what your peers are saying about ABBYY, WorkFusion, Automation Anywhere and others in Intelligent Document Processing (IDP). Updated: January 2022.
564,643 professionals have used our research since 2012.