While unstructured data accounts for 80% of an organization’s content and information generation will continue to grow, businesses will need more powerful solutions that can automate the entire process of capturing, extracting, and processing content with minimal human intervention, resulting in intelligent document automation.
Intelligent document processing is the automation of data extraction from unstructured documents. This can be done through a variety of different methods including optical character recognition, natural language processing, and machine learning.
It is considered one of many automated data capture methods available.
According to Gartner, companies globally increase their use of paper by 25% per year. Without automation solutions, organizations need to scan paper documents to create an image of a document, and employees need to manually extract information in order to organize and decrease the time required to retrieve these documents in the future.
However, with the technology advancement, new methods for automatically extracting information from documents, such as OCR and ICR, were developed.
In order to meet their difficult intelligent document automation and digitization demands, businesses want more complicated, adaptable, and precise solutions than OCR software.
In this post, we will define Intelligent Document Processing (IDP), compare it to other document capture approaches, and discuss the benefits it provides companies.
What is Intelligent Document Processing?
Intelligent document processing, also referred to as intelligent data capture, is the automation of data extraction from complicated semi-structured/unstructured documents and transform it into structured useable data providing end-to-end automation to document-centric business practices.
Intelligent document processing systems convert unstructured data by utilizing technologies such as Artificial Intelligence (AI), Machine Learning (ML), Optical Character Recognition (OCR), and Intelligent Character Recognition (ICR) to classify, categorize, extract, and validate the extracted data.
The majority of well known document management systems have a robust IPD functionality integrated within their application to make it easier for users to extract information automatically thus speeding up the time needed to organize documents.
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The bulk of organizations’ information is unstructured, and it contains important data that businesses must comprehend and use in order to continue to improve, learn how to improve their customer experience, modify their business model, or just study data.
IDP can automatically identify through all of this data, extract important information, classify it, and drive the flow of information for simpler management and better business decisions.
Always keep in mind that data is an organization’s most important asset, which this technology makes instantly accessible for business operations processing.
In today’s increasingly digital and automated world, the ability to extract data within documents is becoming increasingly important.
Intelligent document processing (IDP) is gaining popularity because it offers innovative solutions for automating intelligent data extraction tasks that were previously exceedingly difficult,
Why Intelligent Document Processing is Important?
IDP is gaining attention as it helps organizations reduce costs, enhance accuracy, speed up automation, improve productivity, and simplify compliance.
IDP helps organizations integrate with other core business applications, minimize human intervention, handle challenges associated with reading complicated document formats, and fulfill legal and compliance requirements.
Despite having a wealth of data, the largest problem companies face today is leveraging this data in a responsible way that is most relevant to their performance.
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What is the difference between OCR and IDP?
OCR and IDP are both important technologies for extracting information from unstructured data sources. OCR is used to convert images of text into machine-encoded text, while IDP is used to detect and extract information from natural language documents.
Even though IDP and OCR are frequently used interchangeably, there is a big difference between these terms.
IDP was created to address the shortcomings of OCR, especially its inability to extract data from complicated documents.
To summarize, OCR is a subset of IDP, but not the other way around.
Intelligent Document Processing Components
IDP will be able to detect, categorize, and extract distilled information, which will then be sent to the appropriate document workflows for review.
To effectively process complicated documents automatically, IDP follows three phases.
The first step is intelligent document capture. As a prerequisite, scanning should already be in place to convert paper documents—physical mail—into digital images.
Using technologies like AI, ML, OCR, and ICR, relevant and important data will be captured from these documents.
With the help of these technologies, semi-structured and unstructured documents can be processed and it has increased the accuracy of data being extracted.
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In this phase, the processor pulls 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.
The artificial interpretation of information is critical to successful data extraction. Because AI is only as intelligent as its training, the system must be able to locate and classify all anticipated information inside a document.
To assure the correctness of the processing outputs, the extracted data is subjected to a number of automatic or manual validation tests.
IDP systems are distinct in that they utilize external databases to verify the information. Any information that does not match is highlighted for human inspection and correction.
The collected data is then compiled into a final output file, which is commonly in JSON or XML format. APIs are used to send the file to a business process or a data repository.
The collected information should be saved somewhere or transmitted to be processed by automated business processes.
Many solutions provide interfaces with CRM, ERP, and DMS systems, allowing extracted data to be automatically saved, organized, and secured in these systems.
Intelligent Document Processing Use Cases
Intelligent document processing can be used for a number of different use cases, including:
1. Automated data extraction
IDP can be used to automatically extract data from documents, including unstructured documents such as PDFs. This can be used for a number of different purposes, including extracting data for use in business intelligence or data analytics applications.
2. Automated document processing
It can also be used to automatically process documents. This can include tasks such as automatically categorizing documents, extracting data from documents, or even automatically generating documents based on templates.
3. Automated contract management
IDP can also be used for automated contract management. This can include tasks such as automatically extracting data from contracts, automatically generating reports on contract performance, or even automatically generating contract renewals.
4. Automated customer service
IDP can be used for automated customer service. This can include tasks such as automatically extracting data from customer service requests, automatically generating customer service responses, or even automatically generating customer service reports.
Organizations are increasingly trying to automate their workers’ monotonous and time-consuming activities in today’s digital environment, particularly following COVID-19, to let them to focus on more essential subjects.
Employees will spend a significant amount of time retrieving important business information as the amount of data created continues to grow. Scanning and other technologies have shown to be useful in many situations, however complicated unstructured documents require a superior method of processing.
Intelligent document processing has enabled the automation of corporate operations and the improvement of overall efficiency and productivity.