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  • Sidharth Sahu

Intelligent Document Processing (IDP): The Ultimate Guide

In today’s digital age businesses must deal with an immense volume of data and documents on a daily basis, presenting challenges in effectively managing and processing information. AI enabled technologies have emerged as an increasingly crucial solution — no more so than Intelligent Document Processing (IDP).

Using advanced machine learning algorithms, IDP automates document processing from input to data extraction, saving businesses time and resources while improving data accuracy. In this ultimate guide, we’ll take a deep dive into IDP, covering everything from its functionality, to how document processing has evolved beyond Optical Character Recognition (OCR).

What is Intelligent Document Processing (IDP)?

Intelligent Document Processing (IDP) leverages artificial intelligence technologies to replicate human capabilities in identifying, contextualising, and processing documents — regardless of the quality, layout, or file type. This includes the processing of structured data and unstructured data that comes from any document source including scanned images, PDFs, word processing files, and online forms.

Structured vs. Unstructured Data

How IDP Works

IDP combines a powerful suite of intelligent technologies to perform complex document processes, including Optical Character Recognition (OCR), machine learning and natural language processing (NLP). Let’s take a closer look at each step in the process.

Document Capture and Classification

The first step is to capture the document(s) to be processed. This can be done automatically via. our API or through manual methods such as scanning, uploading files, or emails. Once the document is captured, it’s automatically classified based on its type, such as invoices, purchase orders, contracts, or receipts. This enables the IDP system to understand the layout and structure of the document.

Data Extraction

After the document is classified, the relevant information is extracted from the document. This can be any entity you specify, such as names, addresses, dates, and amounts. The process is powered by machine learning algorithms that on average, deliver 99.5% accuracy on data extraction. The models are also self-improving, meaning that they learn and improve over time.

Data Validation

The extracted data is then validated against predefined rules or business logic to ensure accuracy — ruling out any errors or inconsistencies that may have occurred during the data extraction process. Discrepancies or errors are flagged and presented for human review and correction.

Data Export

Once the data is validated, it can be automatically exported to other systems or applications, such as accounting software or databases.

Beyond Optical Character Recognition (OCR), How IDP is Advancing Document Processing

IDP and OCR are related technologies but have some fundamental differences. OCR extracts predefined data from scanned images or documents. Essentially, it converts images (with text) into machine-readable text. OCR is often used interchangeably with IDP, but it’s important to understand the difference, as OCR has several limitations that IDP solves.

Limitations of OCR

Optical character recognition (OCR) is a technology that has been used for many years to automate tasks, such as data entry or document digitisation. However, OCR is a limited technology that only recognises text and does not understand the context or meaning of the document.

Let’s take a deeper look into the limitations that OCR poses and why organisations are adopting IDP to streamline their business processes and improve efficiency.

Human Dependency

OCR technology is susceptible to inaccuracies and may necessitate human intervention to verify the data. Additionally, it must be trained to identify the location of data within a new file format, which can be time-consuming when integrating new documents.

Template Restrictions

OCR technology is effective when processing simple documents that adhere to a set template. However, even minor deviations from the template can result in data extraction failure.

Contextualisation Limitations

OCR technology is limited in its ability to extract contextual information from the data it processes, as it simply converts the scanned or captured document into machine-readable text. As such, OCR is not an optimal solution for end-to-end automation, as it cannot fully understand the meaning or context of the information being extracted.

Formatting Restrictions

OCR solutions are not suitable for unstructured and semi-structured documents. They can have difficulty recognising text that is formatted in unusual ways, such as text that is rotated, distorted, or has varying font sizes or styles. This can result in errors in the final output or require manual correction, which can be time-consuming and costly. OCR solutions also fail to recognise text from handwritten documents.

Feature Comparison: OCR vs IDPIn Summary

IDP incorporates OCR as one of its components, along with machine learning and natural language processing (NLP) — providing greater data accuracy and new capabilities for businesses.

Not only does it extract text from scanned images or documents, but it also uses artificial intelligence and machine learning algorithms to understand the context — enabling it to identify the data you need, regardless of how it’s presented.

This accommodates variability in how the data is structured and the legibility of the data.

IDP goes beyond OCR by extracting and validating relevant data from unstructured and semi-structured documents, such as invoices, contracts, and receipts. IDP can also classify documents based on their type, enabling businesses to automate document processing tasks and streamline their workflows.

In summary, while OCR is a basic text recognition technology, IDP is a more advanced document processing technology that uses OCR and other techniques to extract, validate, and classify data from documents.

Visit our website to learn more or book a demo of our IDP solution.

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