Enterprise organizations are increasingly making use of document data extraction to study, classify and better utilize their structured, semi-structured, and unstructured data. This is evident in the compound annual growth rate forecast of 30.1% till 2030 for Intelligent Document Processing (IDP), the parent category of document data extraction. 

The power of intelligent document data extraction lies in its flexibility to handle a wide variety of documents and data sources ranging from structured relational databases to semi-structured webpages to unstructured emails, images, and videos. 

While intelligent document extraction is not a technology by itself, it is enabled by multiple cutting edge and established technologies and intelligent document processing platforms. Notable among these are Robotic Process Automation (RPA), Optical Character Recognition (OCR), Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision (CV) along with the use of Intelligent Document Processing (IDP) platforms. 

Technologies for Extracting Structured Data Vs. Those for Extracting Unstructured Data 

All data is not created equal. As a result, the technologies to extract data also vary depending on whether the data sources are structured or unstructured. 

Extracting data from structured data sources is more straightforward than doing so for semi-structured or unstructured data sources. 

Data found within spreadsheets and online questionnaires and surveys can be said to be structured as they adhere to a certain template or pattern. On the other hand, data found within emails, images, videos, and scanned copies of handwritten invoices are unstructured as they do not follow a set template and exhibit variations in style and patterns. 

Extracting structured data is a relatively straightforward affair that involves using OCR and RPA with predefined rules to gather the required information and organize it. 

Gathering useful information from unstructured documents is a more complex task that makes use of OCR and RPA along with machine learning, pattern recognition, and natural language processing in tandem. The machine learning component makes a major difference in identifying useful information in an unorganized structure while natural language processing helps convert extracted text to a machine-readable format.

What are the Technologies Behind Intelligent Document Data Extraction? 

Document data extraction becomes a truly intelligent process only with the combination of certain cutting-edge and established technologies. 

Intelligent document data extraction involves a combination of Robotic Process Automation (RPA), Optical Character Recognition (OCR), Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV) along with the use of Intelligent Document Processing (IDP) platforms 

Let’s take a closer look at each of these.

Robotic Process Automation (RPA) to Simulate Manual Data Extraction from Documents 

Traditionally, document extraction has made use of RPA to simulate manual extraction using a step-based approach. The limitation of document extraction only using RPA is that it is pure automation without the intelligence of a human to discern between document formats and layouts. The infusion of other technologies, particularly AI, overcomes this limitation.

Optical Character Recognition (OCR) to Make Image Text Machine Readable 

Optical Character Recognition, another core technology behind automated data extraction, makes text on images machine readable. It allows one to copy and manipulate text that is in image or PDF format. For example, OCR allows to convert text from screenshots or handwritten documents into a format that can be used online or on preferred software. 

Artificial Intelligence (AI) for Intelligent Document Processing 

Artificial Intelligence technologies allow computers to understand a variety of human inputs and intelligently respond to queries based on them. In the context of document processing, AI allows automated systems that process documents to be given specific queries and provide responses to them. AI is what makes document process automation intelligent. 

Machine Learning (ML) for Pattern Recognition 

Machine Learning is a subset of AI. It involves repeatedly training algorithms to learn from data fed to them. In the realm of document extraction, ML helps in recognizing patterns and templates within data sources like documents and images, extracting only relevant information, and in improving extraction accuracy over time. Machine learning is what makes it possible to identify data points like names, dates, and invoice amounts within documents. 

Natural Language Processing (NLP) for Unstructured Data Extraction 

Natural Language Processing focusses on the interaction between machines and human language. It involves programming computers to process and comprehend natural human language. NLP makes use of AI techniques like sentiment analysis and text classification to analyze blocks of text and extract useful insights from unstructured documents like emails and handwritten documents. NLP makes it possible for AI to understand the context and meaning within entire documents, not just of individual words. 

Computer Vision (CV) to Mimic Human Vision 

Computer Vision is a subset of artificial intelligence that mimics human vision to process digital images and other types of visual inputs to comprehend patterns, make decisions, and act based on what is seen. It makes use of machine learning models to interpret what is seen. Computer Vision can observe and make sense of specific strokes and patterns found in handwritten text and drawn images. 

Intelligent Document Processing (IDP) Platforms 

Dedicated Intelligent Document Processing (IDP) platforms integrate RPA and other technologies like OCR, AI, ML, NLP, and computer vision to make intelligent document data extraction possible. They improve in accuracy over time and can handle unstructured images, emails, and PDFs in addition to structured data. 

What Does Intelligent Document Data Extraction Using Multiple Technologies Look Like? 

The intelligent document data extraction process brings together multiple technologies over five steps to make information from data sources actionable. 

These are the steps involved:  

First, the documents to be extracted are gathered and prepared. This step may involve tasks like enhancing images and reducing noise to improve accuracy when the extraction takes place.  

Secondly, if there are scanned images or PDFs, these are converted to text with OCR technology.  

Next, relevant data points and information from within each document are identified for extraction.  

The fourth step is where the actual extraction takes place. Multiple techniques including pattern matching, parsing, and extracting based on rules are used to accurately extract the required data. Accuracy at this stage is improved if machine learning algorithms have been used to train the extraction engine.  

The fifth and final step in the process involves verifying and validating the extracted data to ensure accuracy and consistency. The extracted data is compared against pre-defined rules for validation and data quality checks are performed. 

The Power of Intelligent Document Data Extraction 

As evident, intelligent document data extraction is not a monolith but rather a pot-pourri of multiple technologies that work together intricately to enable it. Its power goes beyond merely providing the ability to extract useful information and extends to making the extracted information actionable through automation. 

A good document processing services provider will use multiple algorithms to boost extraction accuracy rates, achieve robust, high-accuracy output and harness various visual algorithms to help with object or face recognition. It will be able to perform data extraction from business documents as well as email data extraction and extraction and processing from images and videos. 

Ultimately, document data extraction is an evolving area and embracing the right technologies can make all the difference. 

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