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Overview of Intelligent Document Processing

Digital Transformation – a trend that has taken operation optimization to new heights. In this digital age, the dependence on technological solutions has grown to a point where it is nearly impossible to sustain businesses without some involvement of technology. It is only logical that automation at the ground-root levels is necessary and unavoidable.

Historically, document processing was a manual, rigorous, and monotonous process and employees felt unrewarded after hours of continuous work. Bent backs, strained eyesight, and the sheer dullness of repetitive reproduction of data often made employees indifferent to inaccuracies of records.

As the digital age came around, organizations began looking for solutions to simplify this process and the concept for ‘Intelligent Document Processing’ took root. Intelligent Document Processing (IDP) is as unambiguous as it sounds. It is a digital solution to process data obtained from documents into sensible, easy-to-access formats and is an integral part of the digital transformation journey of an organization.

Even though the initial objective of IDP was to streamline document processing, it has now evolved into integrating a human understanding of unstructured or semi-structured data with automation technology. Developers also aim to enhance robotic process automation with the additions IDP offers. The amounts of data that businesses generate is huge, but often useless. Lack of processing capability or workforce leaves data uncatalogued and unused. IDP allows big data handling, accommodates different levels of structure and different formats of content.

Intelligent Document Processing and Document Capture Technologies – Where they converge.

Document Capture technology is the foundation of the technology that we know and use today. It is still an integral part of IDP software. IDP is incomplete without document capture technology, but incorporates major improvements, leaving the primitive technologies redundant. IDP can change document formats, classify data, recognize characters and symbols, and most importantly, be scaled to the requirements of a business. Developments in Artificial Intelligence (AI), Natural Language Processing (NLP), Machine Learning (ML), Deep Learning, and Optical Character Recognition (OCR) have made it possible for IDP to become versatile and widely applicable.

Keeping this in mind, an all-inclusive explanation of IDP is:

“Intelligent Document Processing is a smart solution that assimilates human understanding of data and technological accuracy. It is the part of intelligent process automation that utilizes OCR, AI, NLP and Machine Learning to process large volumes of data and documentation by extracting, recognizing and classifying data from a wide range of formats.”

So, how does IDP work?

Document Scanning

This process digitizes physical documents. Document capture technology combined with scanning hardware convert documents to a digital or electronic format. In case of electronically genrated documents, this step becomes unnecessary.

Image Processing

Once documents are in a digital format; the quality of image needs to be standardized. Hence, this process undertakes tasks such as noise reduction and binarization to enhance quality of image and make it easier to perform OCR.


Using AI, pre-processed documents are analyzed to detect important information. In many instances, multi-page documents hold information that is not necessarily relevant to a database. Such data is eliminated in this process. Documents are broken into smaller parts and only the important pages are kept.
This step also involves classifying different documents and retaining different parts for different functions. This may involve duplicating pages and categorizing them according to end-use.

Optical Character Recognition

This is done using machine learning algorithms. OCR software is capable of recognizing characters and symbols from documents and inserts them into the correct fields.
Alternatively, Barcode Recognition, ICR (Intelligent Character Recognition) and OMR (Optical Mark Recognition) can also be integrated to make IDP foolproof.


OCR and pattern matching makes it possible for IDP software to extract exact data such as names, dates, age, account number or invoice number from a document and organize them in a structured, prescribed format.

Verification / Validation

Once data is extracted; the software highlights any information that is missing or incorrectly formatted for human agents to review and correct.

Advantages of IDP

  • The addition of intelligent document processing to a system that uses robotic process automation can result in complete digital transformation and end-to-end automation.
  • IDP eliminates the necessity for human agents to manually sort through and regenerate information. The multiplicity of tasks carried out is reduced as the system carries out most processes internally.
  • Since IDP software can be trained to learn continuously, with time, they become more efficient. Machine learning technology enables the system to correct errors that may have previously occurred and adapt to the content as more learning opportunities are presented.
  • As these systems collaborate with humans, the human capability of logical deduction is added to technological efficiency. Human capabilities are supported by and extended to computers resulting in the formation of intelligent tools and solutions.
  • IDP can be used to process invoices, purchase orders, insurance claims, application forms, KYC documents. However, their usage is not limited to these areas and can be adapted to an array of functional fields.

Intelligent document processing has the potential to bring automation to a level where humans can be taken out of the loop of mundane tasks. As organizations step into the digital movement and automation helps improve productivity, solutions such as IDP and RPA become obvious and irreplaceable elements of organizational structure.