Intelligent Data Extraction: What Is It and Why Should You Adopt It?

Learn more about Intelligent Data Extraction, the working process, use cases, the future trends and how you can benefit your busi

 min. read
May 28, 2024
Intelligent Data Extraction: What Is It and Why Should You Adopt It?

Nowadays, businesses have access to abundant data resources, both structured and unstructured. This vast data comes with the potential to unlock valuable insights, improve decision-making, and drive innovation. But there's an issue - traditional data extraction methods often fall short in harnessing this potential due to their efficiency, accuracy, and scalability limitations. That's where intelligent data extraction (IDE) steps in as a transformative solution.

Employing advanced techniques like Machine Learning, NLP, and more, IDE empowers you to efficiently and accurately extract meaningful information from diverse data sources. As a result, it opens doors to enhanced productivity and streamlined processes and gives you a competitive edge in the digital age. Continue reading to learn more about intelligent data extraction, its benefits, use cases, and more!

Intelligent data extraction is a process that involves automating the extraction of valuable information from various types of data sources. These include documents, emails, forms, images, and more.

Generally, IDE leverages a combination of AI techniques to ensure the accuracy of extracted data. These are natural language processing, machine learning, and computer vision to recognize, understand, and extract data points efficiently and quickly.

Step-by-Step Working of Intelligent Data Extraction

Here's the step-by-step guide on the working of an IDE system:

Image Pre-Processing

The first step in intelligent data extraction is image pre-processing, ensuring the input data is ready for accurate extraction. The following processes happen at this stage:

  • De-Skew: The process often begins with de-skewing the input image. De-skewing corrects any rotational misalignment in scanned or captured images, ensuring the text or content is horizontally aligned for accurate processing.
  • Binarization: It's a fundamental image processing technique that converts a grayscale image into a binary image, where each pixel is classified as either black or white based on a specified threshold.
  • Zoning: After de-skewing, the image is typically divided into zones or regions. This segmentation allows the system to focus on specific areas of interest within the document, improving accuracy and efficiency.
  • Normalization: Normalization involves standardizing the image quality and size. This step can include adjusting contrast, brightness, and resolution to enhance the clarity of the content, making it easier for subsequent processing steps.

Document Classification

After the images are optimized to maximize extraction accuracy, data classification comes into play. This phase categorizes documents based on their content, format, or purpose.

Classification ensures each document is routed to the appropriate processing pipeline, allowing for tailored data extraction and validation procedures. For instance, invoices are classified differently from contracts, guiding the system to apply the relevant extraction rules.

Data Extraction

With documents categorized, the core data or information extraction process commences. This step involves using AI technologies like optical character recognition (OCR), machine learning (ML), and large language models (LLMs) like GPT-4 to not just extract the texts from images but more importantly convert the extract results into structured formats like JSON, XML, or CSV so that they can be directly imported into different software or platforms for further processing or analysis.

Post-processing of the Output

Next, the output undergoes post-processing to refine and enhance the accuracy of the extracted data. Post-processing may involve error correction, ambiguity resolution, and overall data quality improvement.

Techniques such as spell-checking and grammar checks can be applied to ensure that the recognized text is error-free and contextually accurate. This final step in intelligent data processing aims to deliver high-quality, reliable data that you can readily use for decision-making.

Due to its versatility, intelligent data extraction use cases are diverse and span various industries. Here are some intelligent data extraction examples in different fields;

Legal Sector

Legal firms are increasingly turning to IDE to automate the extraction of critical information from legal documents, contracts, and court records. This automation expedites document review processes and enhances accuracy, which is crucial in legal contexts.

Supply Chain Management

Supply chain management deals with a ton of paperwork, often unstructured and in various formats, like invoices, purchase orders, and customs forms. Intelligent information extraction categorizes and processes these documents automatically, saving time and eliminating the need for manual format adjustments.

Banking

Banks handle heaps of paperwork they deal with daily, from loan applications to customer records. IDE makes it easier to extract and process essential information from these documents without time-consuming manual work. For instance, when a new customers apply to open new accounts, they often have to fill out the onboarding forms. With IDE, they simply have to upload images of their personal document and the forms will be filled with the extracted data. This boosts efficiency, reduces errors, and provide a better customer experience as documents are processed much faster.

To make data extraction intelligent, many AI technologies are incorporated. Here are some of the techniques:

Natural Language Processing

NLP is an essential component of IDE, especially when dealing with unstructured textual data. NLP's focus is on enabling machines to understand, interpret, and generate human language, making it invaluable for extracting insights from documents. The following are NLP techniques employed in IDE:

  • Tokenization: Breaking down text into smaller units, such as words or phrases, for analysis.
  • Named Entity Recognition (NER): Identifying entities like names, dates, and locations within the text.
  • Sentiment Analysis: Determining the emotional tone or sentiment expressed in text data.
  • Part-of-Speech Tagging: Labeling words in a sentence as nouns, verbs, adjectives, etc., to understand grammatical structure.

OCR (Optical Character Recognition)

Optical Character Recognition is fundamental for IDE as it converts printed or handwritten text within images or scanned documents into machine-readable text. It is crucial in digitizing printed documents, extracting data from invoices, receipts, and forms, and enabling text search within scanned files. Overall, OCR enhances the accessibility of information stored in paper-based or image-based documents, making it readily available for further analysis and processing.

Data Mining

Data mining is employed in IDE to uncover patterns, relationships, or insights within large datasets. Techniques include association rules to discover relationships between data items, clustering to group similar data points, and text mining for extracting information from textual data.

Data mining significantly impacts the process of planning business strategies and operational management. It also benefits customer-centric functions like marketing, advertising, sales, and customer support, as well as areas like manufacturing, supply chain management, finance, and HR.

Machine Learning

Machine learning is helpful for data analysis and visualization. It plays a pivotal role in IDE by training algorithms to recognize and extract valuable information from various documents. Furthermore, it involves two learning types!

These include - supervised learning, where models are trained with labeled data to recognize patterns; unsupervised learning for identifying structures without labels; and deep learning, using neural networks for complex tasks. In IDE, machine learning automates tasks like invoice data extraction, handwriting recognition, and document categorization.

The benefits of intelligent data extraction are many, and some of these are:

Improves Organizational Synergy

Intelligent Data Extraction acts as a cohesive force within an organization by enhancing collaboration and knowledge sharing. When employees are relieved of manual data entry and extraction tasks, they can redirect their efforts toward more strategic and creative endeavors. This improved synergy leads to better cross-functional cooperation, where departments can access accurate and up-to-date data swiftly.

Saves Time by Eliminating Tedious Tasks

One of the most tangible benefits of IDE is the gift of time. It automates data extraction from documents and liberates employees from the drudgery of repetitive and time-consuming tasks. As per Forbes, AI-based extraction techniques save around 30% to 40% of the hours spent on extraction tasks.

In sectors like healthcare, IDE can spare medical professionals from hours of manually transcribing patient records. This allows them to allocate more time to patient care and critical medical tasks. Furthermore, it accelerates tasks such as automating finance data extraction and invoice processing, enabling quicker approvals and payments.

Reduces Operational Costs

IDE is not just a time-saver but also a cost-saver. Automating data extraction can significantly reduce operational costs associated with manual data entry errors and time delays.

For example, in supply chain management, IDE streamlines the processing of invoices, purchase orders, and shipping documents. This cuts down on processing time and minimizes costly errors that may occur during manual data entry.

Some of the common challenges and considerations in implementing intelligent data extraction are:

Data Quality and Consistency

A significant challenge in IDE implementation is managing the variability in data sources. IDE often deals with data from diverse origins, each with its unique formatting and structure. Ensuring the accuracy and consistency of data extraction across these sources can be challenging.

Security and Compliance

Intelligent data extraction software frequently handles sensitive information, making data privacy and compliance with regulations like HIPAA paramount. Robust cybersecurity measures need to be there when implementing an IDE solution to protect against potential threats.

Training and Maintenance

Intelligent data extraction solutions require initial training and ongoing updates to maintain accuracy. This training process can be resource-intensive and requires a commitment to data curation and model refinement. Furthermore, regular system maintenance is essential to ensure IDE systems perform optimally over time.

FormX is the platform that takes care of all these challenges, offering you comprehensive intelligent data extraction solutions. You don't have to worry about maintenance, training, privacy, and data quality, as FormX takes care of everything.

In the near future, intelligent data extraction is set to undergo significant advancements. Automation will take center stage as systems become more self-sufficient. It'll reduce the need for human intervention in training algorithms and verifying results.

Also, expect an uptick in accuracy as machine learning algorithms continue to improve. This allows businesses to handle critical tasks like financial reporting and compliance with these systems. Furthermore, the range of applications for intelligent data extraction will expand, venturing into areas like predictive analytics, fraud detection, and customer experience analysis.

The best part - integration with other technologies, such as robotic process automation (RPA) and virtual assistants, will create more seamless user experiences. These developments promise to revolutionize data handling and decision-making in various industries.

FormX is an exceptional Intelligent data extraction software, setting a new standard for precision and efficiency in document processing. It boasts pre-configured data extraction models tailored for diverse document types. These vary from shopping receipts to business certificates and bill of lading, making it a fantastic choice for accurate data extraction with unmatched ease. This IDE solution also takes the complexity out of document data handling, offering an effortless API integration.

The extracted results are returned in JSON or XML, making it both developer-friendly and business-savvy. The user-friendly web portal is an intuitive playground, enabling users of all backgrounds to configure parsing settings. You can also use it to test data extractions and access integration resources seamlessly. All in all, with FormX's mobile scanning capability and optimization processes, data extraction accuracy reaches new heights.

Contact us today or sign up for a free trial to see how FormX can make your data extraction process intelligent and automated.