Automate processes with intelligent document solutions

Use intelligent document processing to convert unstructured data into structured information and gain a competitive edge
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Leverage intelligent document solutions to reduce manual and error-prone work

Automated document processing streamlines business processes, lowers costs, and improves efficiency. IDP helps you to transform large amounts of unstructured data into structured information, while maintaining high reliability and accuracy.

Reduce the need for a big human workforce with IDP

Answer business needs using customized machine learning and AI technologies
  • Accelerate document processing

    Use scalable computation to quickly extract relevant info

  • Optimize costs

    Free up human employees for high-value activities

  • Reduce errors

    Leverage IDP techniques to streamline processes and make them more error-resilient

  • Scale document processes easily

    Increase computational power at the click of a button in periods of high data velocity

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We’ve had a long-term relationship with Netguru. Netguru is a great and super-professional service provider, which brought new technologies, new methodology, and a fresh perspective to our project.

Assaf Davidi

VP Product at temi

Use IDP to uncover valuable business insights

Analyze converted text data to make data-driven decisions and streamline business operations.
  • Information extraction

    Use NLP and machine learning to derive actionable insights from unstructured and semi-structured data

  • Sentiment analysis

    Leverage opinion mining to scan data & establish its status: positive, negative or neutral

  • Named entity recognition

    Utilize NLP to automatically scan text, identify fundamental entities, and classify them

  • Text classification

    Automatically analyze text and assign predefined tags or categories

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What is intelligent document processing and how does it work?

Intelligent document processing automation (IDP) is a set of machine learning, natural language processing (one of the main machine learning subfields), and artificial intelligence techniques, used to extract data from documents.

IDP is often assisted by optical character recognition (OCR). It can deal with any type of document: digitally typed, handwritten, or scanned. Because documents often contain pictures and text, computer vision algorithms are used as well. There are several standard steps, with specific cases requiring fewer or more stages:

  • Pre-processing to transform documents into machine-readable formats
  • Classification to determine which document parts should go to particular workflows
  • Intelligent data extraction to retrieve insights from documents
  • Post-processing to validate extracted data

What techniques are used in intelligent document solutions?

IDP software uses robotic process automation, artificial intelligence, machine learning, and natural language processing to reduce or even eliminate manual processing and the associated errors that occur when humans carry out repetitive tasks.

Intelligent document processing solutions unlock the value of unstructured data. How? By transforming it into high-quality, structured, and relevant information that can be further analyzed.

Specific techniques that are used within IDP are:

  • Information extraction. This NLP approach involves retrieving info relating to a selected topic from unstructured data or semi-structured data.
  • Sentiment analysis. It's a NLP technique that scans relevant data to monitor things like consumers’ opinions of products and services, customer experience satisfaction, and how a company is perceived on social media. For example, are people happy, neutral or unhappy with a product or service?
  • Named entity recognition. Aka entity identification, entity extraction, or entity chunking, NLP is used to automatically scan text, identify entities (main components of a sentence), and classify them into predefined categories such as names, dates, and times.
  • Text classification. Aka text tagging or text categorization, this is a foundation for sentiment analysis (and also plays a part in topic detection and language detection). Here, NLP is used as an efficient and effective alternative to manual data entry. It automatically analyzes text, then assigns it a set of predefined tags based on the content.
  • Text similarity. This is a NLP technique that highlights how close two pieces of text are in word construction (lexical) and meaning (semantic).
  • Relationship extraction. This task extracts semantic relationships from text and is an extension of named entity recognition.
  • Text summarization. An NLP technique that condenses info from a large body of text into a smaller, easier-to-consume form. It identifies the most significant sentences and adds them together to create a summary.

What types of data do intelligent document processing solutions work with?

There are three main data structure types:

Structured data: fixed-format documents like application forms and questionnaires. The layout often includes graphical elements such as boxes, checkmarks, and separators, but their position is fixed. Here, simple extraction is sufficient.

Semi-structured data: multi-variant documents with flexible layouts. There’s some visual layout such as boxes, but the format is more flexible, with variants of specific layouts. For example, you may have various invoice layouts from different vendors. This data type requires an IDP solution that can quickly learn new formats and field positions.

Unstructured data: documents with plain, natural language text. In this case, there’s little or no visual organization of text, and whole blocks of text must be read and understood before info is extracted. Because this is the most complex data type, it requires segmentation, entity extraction, and large volumes of data samples. Intelligent document solutions thrive in this type of data.

What are the types of data that can be encountered during intelligent automation projects?

There are three main types:

  • Plain text: the least complicated
  • Parsable: things like DOCX files and text PDFs. These are in text format and just need to be parsed by the computer into plain text.
  • OCR requiring: examples include pictures and PDFs created from pictures. These are more complicated, depending on the quality of the picture. The parsed text can contain errors. It gets converted into plain text in the end.

What is the difference between OCR and IDP?

Optical character recognition (OCR) is a data conversion technique whereby an image of text is converted into a machine-readable form. This long-standing method is the basis of document scanning. But, OCR typically can’t extract context from the content, making automated data extraction and interpretation impossible.

Following advances in automated document processing, OCR is now a sub-process of IDP. Here are the steps:

  • OCR converts an image of text into a machine-readable form
  • Document processing using machine learning and AI document processing recognize and capture the content from unstructured, semi-structured, and structured sources
  • Context is extracted
  • Essential data insights are generated

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More IDP-related questions?

Check frequently searched intelligent document processing queries.
Do I need a custom IDP solution?
If you mainly process structured documents with simple content and want to complete routine tasks, ready-to-use IDP solutions are the best option.

But, if you have a lot of documents containing only plain text, invoices coming from different vendors, or you want extra tasks to be performed, a custom document processing solution designed to meet your specific needs is the way forward.

Intelligent document solutions aren't a standard only for financial institutions, insurance or legal industry. They can be applied with great benefit for any type of business process concerning major document flows.
How does intelligent document processing accelerate your business?
In a nutshell, tasks are performed faster. Text doesn’t have to be read and thoroughly analyzed by a human. Instead, these dull and repeatable tasks are processed by a machine that outputs results in seconds. The results are repeatable and dependable, leaving your employees to concentrate on more productive work.
Can I create a custom IDP solution with limited data on my side?
It depends, but most of the time, with the use of pre-trained language (transformer) models, we can develop document processing services for a client’s needs with very little fine-tuning data needed.
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