Traditional ways of document processing are prone to error, costly and are more time-consuming. The world has undergone a digital transformation and has turned digital in every possible way. And today, people need information not only quickly, but they will likely use the information within those documents for downstream applications. Yet, many organizations spend much of their time manually processing information from unlimited documents. Most business data is embedded in files such as PDFs, spreadsheets, or excel sheets. Because of the nature of the files, even multimedia such as video, various facts and figures have to be processed and entered by hand.

But with time, evolution is bound to happen. Many companies dealing with digital data are finding it essential to convert manual data and derive complex content from a variety of document formats. These documents can be integrated into day-to-day business processes using document processing as it seamlessly transforms manual and analogue data into a digital format. It makes use of Artificial Intelligence (AI) technologies such as Natural Language Processing (NLP), Computer Vision, deep learning and Machine Learning (ML) to classify, categorize, and extract relevant information and validate the extracted data. A company can digitally replicate the document’s original structure, layout, text, and images by using a document-processing system to extract data.

Document processing is best suited to convert documents with identical formats. But there might be cases where it may become difficult for organizations to recognize the formats. This case scenario may call upon human intervention to complete the process. Hence, Intelligent Document Processing (IDP) was introduced to improve effectiveness and efficiency. Let’s discuss in detail about IDP in this blog.

Definition

AI can understand the semantics of content and possess the ability to acquire knowledge, thus improvizing effectiveness and efficiency automatically.

IDP is defined as the next generation of automation that captures, extracts, and processes data from a variety of document formats. In short, IDP is the process of data extraction automation from unstructured and semi-structured documents that can be transformed into structured and usable data.

IDP acts as a medium of software solutions that use the power of AI and ML technologies.

These technologies can help process all types of documents, extract relevant information, and then pass the output into downstream applications such as process automation solutions and document management, and so on.

According to Gartner data, companies worldwide are continuously increasing their use of paper by 25% each year. Without automation solutions, organizations need to scan paper documents, and employees need to manually extract information to organize and decrease the time required to retrieve these documents in the future.

IDP uses ML, AI, Optical Character Recognition (OCR), and Intelligent Character Recognition (ICR) technologies to classify, categorize, extract, and validate the extracted data.

Deloitte defines IDP as: “IDP automates the processing of data contained in documents ‚Äï understanding what the document is about, what information it contains, extracting that information, and sending it to the right place.”

Benefits of using IDP

Following are the benefits of using IDP over traditional document processing methods –

Faster data processing: Extracting relevant information from unstructured and analogue data becomes easier. This helps eliminate manual processes and reduces errors, resulting in cutting workflows.

Unstructured document processing: With the help of AI and ML techniques, IDP can transform structured, unstructured, or even semi-unstructured information correctly. Businesses can successfully utilize this information in applications and workflows.

Increase in data accuracy: Quality and reliable data is processed using ML, which enhances document classification, information extraction, and data validation. Implementing low-code supervised training within the workflow improves accuracy over time without recoding extraction rules.

Staunch security: IDP plays an important role in storing documents and personal information in a secure digital location. Industries like finance and healthcare services consider it essential to have strict security regulations and compliance policies.

Cost-effective: Traditional ways of document processing takes much of human time, thus taking away much of experts’ time. IDP shortens processing time, thus reducing operational costs with better utilization of human time and brains.

Components of IDP

An IDP system first detects, categorizes, and then extracts distilled information. It is then sent to the appropriate document workflows for further review.

Thus, IDP comprises the following major components – capturing data, extracting data, and validating data.

Data capture

Intelligent document capture is the first step. As a prerequisite, the document must be scanned to convert paper documents such as physical mail into a digital image.

AI, ML, OCR, and ICR technologies are applied to gather relevant and important data. Semi-structured and unstructured documents can be easily processed with the help of these technologies, thus increasing the accuracy of extracted data.

Data extraction

In this step, the processor extracts important information transferred within documents from the output of the first phase and other digital sources by utilizing a pattern matching tool such as Regular Expressions.

For successful data extraction, artificial interpretation of information is essential. An AI system can act intelligently based on its training thus, the system must locate and classify all anticipated information within the document.

Data validation

In this step, the extracted data is subjected to multiple automatic or manual validation tests to ensure the correctness of the processed outputs.

IDP systems are considered distinct as they use external databases to verify the information. If the information does not match, it is passed on to a human for inspection and correction during verification.

Data integration

This is the final step where the collected data is combined into a final output file, mostly in JSON or XML format. With the help of APIs, the compressed file is sent to a business process or a data repository.

The collected information must be first saved and then transmitted, where it gets processed by automated business processes.

Bottom line

Organizations from different fields can benefit from IDP’s capabilities and, thus, save time and boost efficiency. Some of the most widely used applications of IDP are digital document archiving, invoice processing, insurance claims processing, contract administration, fraud detection, mortgage loan, application processing, employees onboarding.

With time, organizations need to automate the monotonous and time-consuming activities of the workers in this digital age. Organizations are continuously trying to inculcate modern practices for their employees, especially after the pandemic, to help employees become more productive.

As the amount of data continues to grow with time, employees will spend a significant amount of time retrieving important business information. No doubt, scanning and other related technologies turned out to be useful in many ways; complicated unstructured files require a superior processing method. The IDP methodology plays a major role in automating corporate operations by improving overall efficiency and productivity.

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