To realize the benefits of digital transformation, converting manual data into electronic documents is an essential step.

The right document-processing solution along with thoughtful, collective planning are essential to accomplish this goal. Document processing involves converting manual forms and analog data into digital files, enabling these to be used as part of day-to-day business processes. A business can digitally recreate a document’s original structure, layout, text and images when utilizing a document processing system. Converting documents with identical formats is ideal for document processing; for unrecognizable or inconsistent formats, the process may need to be redirected to human operators for completion.


Understanding intelligent document processing

As a result of advances in artificial intelligence (AI), companies are now able to automate document processing even further. A technology known as intelligent document processing (IDP) uses artificial intelligence to automatically process documents, extract information and validate data. Through automation and structuring unstructured data, it further automates and speeds up the document processing process.

In addition to robotic process automation (RPA), IDP may also incorporate natural language processing (NLP) tools in order to streamline the process of transitioning from analog to digital and reduce error probability. RPA can automate manual, point-and-click actions, thereby reducing the need for human interaction in the process.


What is the method of document processing?

There are several ways to process documents, including the use of computer vision algorithms, neural networks, or manual processing. A typical digitalization process consists of these steps:

  1. Separate the layout and structure by categorizing   Rules underlie document-processing solutions. In order to begin work, programmers must develop extraction rules. The procedures will include defining the type and format of documents. This will enable the team to determine the layout and structure of those documents.
  2. Determine the information contained within the documents  Teams can automate the transcription process in several ways. A technique called optical character recognition (OCR) scans printed documents and converts the text into data. Handwritten text recognition (HTR), one type of intelligent character recognition (ICR), can recognize both standard texts and various handwriting styles and fonts.
  3. Correct document errors  Using OCR technology may be error-prone, so some extracted data will need to be reviewed manually. A document can be flagged for human review, if it cannot be processed or if errors have been detected, so they can be corrected manually.
  4. Integrate documents and data  Upon completion, the document will be available in a format which will be compatible with line of business applications.

Intelligent document processing is an enhancement to traditional document processing in the following ways:

  • Processing data faster  By using advanced automation, information can be extracted from unstructured, analog, and asynchronous data in a quicker, more accurate manner. In this way, workflows can be shortened, and errors reduced.
  • Managing unstructured documents  IDPs can process structured, unstructured and semi-structured information in a way that applies the information to business workflows and applications, as opposed to traditional document processing.
  • Enhancing accuracy of data  Machine learning enhances the classification of documents, information extraction, and data validation processes to increase the quality and reliability of processing operations. By employing low-code supervised training within the workflow, it is possible to improve accuracy without having to rewrite the extraction rules.
  • Ensuring security  A secure digital location is maintained for documents and personal information. Regulatory and compliance policies are particularly critical in industries like healthcare and finance that are subject to strict security regulations.
  • Cutting costs  Traditional document processing’s manual nature is time consuming and often interferes with other tasks. By automating a process, operational costs are reduced, and staff is more effectively utilized.


Practices that work and challenges that arise

No matter if you are digitizing healthcare records or planning to streamline invoice processing, it is essential to prepare in advance and follow best practices as you get started to avoid costly, time-consuming issues.

These include:

  • Categorization of documents  Documents should be written and organized according to their intended use, which clarifies relative information for concise data extraction.
  • Conversion of data  Data conversion from unstructured and semi-structured data to structured data is an essential step in the automation enhancement process.
  • API planning  How will the digital data be used within the organization once converted to a digital format? Is it easily accessible? Ensure the business requirements are properly aligned within your organization by discussing them with stakeholders.
  • Obtaining the advice of experts – your team  Speak with the users of the information you are digitizing to gain a better understanding of its value, as well as how it should be interpreted. When data is structured in this manner, whoever is handling errors will understand the format of the data and how the process should proceed.

Despite its benefits, traditional document processing is not without its challenges. The following should be considered before embarking on a digital transformation project:

  • Single formats  To convert the relevant information into a digital format, document processing uses predefined extraction rules. In structured data where the information is consistent, this type of data capture works well. Nevertheless, if you have large amounts of unstructured data or complex documents containing inconsistent information, then the process can cause time-consuming errors.
  • Oversight  It is common for processing experts to review issues and errors by hand when they arise. However, this task requires considerable human effort.
  • Process fine tuning  The documentation processing systems lack transparency into how the processing is functioning and what kind of errors are causing sluggish performance.


Application scenarios for document processing

Below are some examples of situations in which document processing can prove useful:

Billing & payroll

Automation and digitization are required in order to achieve digital transformation of billing and payroll systems. In order to automate the process, you can employ a document process automation tool to set up a predefined deep learning model for data extraction.

Insurers & brokers

It is possible to extract data from documents and check coverage and eligibility quickly and easily with document processing. Furthermore, it ensures consistency with industry standards and protocols and protects the confidentiality of sensitive information.


By converting employee and candidate data into valuable insights, you will be able to optimize both hiring and staff management.

Monitoring for fraud

Document processing has proven to be a valuable component of financial services, allowing financial institutions to verify checks and determine the authenticity of high-volume transactions to eliminate discrepancies that may arise in banking transactions.

Residential & commercial loans

Lenders are required to process millions of paper documents each year during mortgage processing. Using document processing facilitates quick and easy document retrieval, thereby increasing the speed and scope of mortgage filing processes.