Artificial Intelligence and Data Extraction (Part 2 of 3)

August 16, 2023
Artificial Intelligence and Data Extraction (Part 2 of 3)

In the previous part, we looked at business motivations that might have brought organizations to the threshold of technology automation projects in the document data extraction segment.  We also covered some potential technology challenges that they need to be aware of before investing in the effort of solution discovery.

Here we look at what technology offerings a business might consider in the context of its needs and what opportunities might be relevant to their particular use case scenarios.  We will also list some of the common preparatory work that needs to be done before an AI product vendor or service provider can be brought into the picture.

AI Products Available for Data Extraction

There are various types of AI products available for data extraction from documents. These products leverage artificial intelligence techniques, such as machine learning and natural language processing, to automate the extraction of data from various types of documents. Here are some common types of AI products used for document data extraction:

  • Optical Character Recognition (OCR) Software: OCR software uses machine learning algorithms to convert scanned documents or images containing printed or handwritten text into machine-readable text. It enables the extraction of textual data from documents and provides a foundation for subsequent data extraction processes.
  • Intelligent Document Processing (IDP) Platforms: IDP platforms combine OCR technology with advanced AI capabilities to automate document data extraction. These platforms typically include features such as document classification, data extraction, data validation, and integration with other systems. IDP platforms often use techniques like natural language processing and machine learning to improve accuracy and handle document variability.
  • Intelligent Data Capture (IDC) Systems: IDC systems are designed to automatically capture data from documents, including structured, semi-structured, and unstructured data. These systems use AI techniques to extract specific data fields or information from documents, such as invoices, receipts, or forms. IDC systems often include capabilities like data validation, entity recognition, and data mapping.
  • Document Understanding AI: Document Understanding AI products combine OCR, natural language processing, and machine learning to understand and extract information from documents in a structured format. These products can handle various document types, extract data fields, and provide contextual understanding for more accurate and intelligent data extraction.
  • Data Extraction APIs: AI-powered data extraction APIs allow developers to integrate document data extraction capabilities into their applications or systems. These APIs provide pre-trained models and algorithms for extracting specific data fields or information from documents. Developers can leverage these APIs to build customized document data extraction solutions or enhance existing applications.
  • Robotic Process Automation (RPA) Tools: RPA tools automate repetitive tasks, including document data extraction. These tools can be trained to recognize and extract data from specific document types, and they often integrate with OCR technology and other AI-based techniques. RPA tools can streamline document-centric processes by automating data extraction and data entry tasks.
  • Industry-Specific Solutions: Some AI products are tailored to specific industries or document types. For example, there are AI solutions specifically designed for extracting data from medical records, legal documents, financial statements, or insurance claims. These industry-specific products leverage domain knowledge and specialized algorithms to optimize data extraction accuracy and handle industry-specific document complexities.

It's important to note that the availability and functionality of these AI products may vary based on the vendor specific requirements, and the complexity of the data extraction task. Organizations would need to evaluate the features, capabilities, and compatibility of different AI products to choose the one that best suits their document data extraction needs.

Pre-requisites for using the AI Approach

Data labeling is a critical step in automating document extraction. It involves identifying and annotating specific elements within a document that the AI model will learn to recognize and extract. Effective data labeling is essential to the accuracy and efficiency of the AI model, as it ensures that the model can accurately identify and extract the desired data from the documents.

Let’s look at some best practices for labeling data for document extraction automation:

  • Clearly define the labeling guidelines: It is important to establish clear guidelines for labeling the data to ensure consistency and accuracy. This can include defining the specific data elements to be extracted, as well as any specific formatting or naming conventions.  It could also cover the part or section of the document from where data needs to be labeled in case of redundancies.
  • Use a consistent labeling approach: Consistency is key when it comes to data labeling. Use a consistent labeling approach across all documents to ensure that the AI model can learn from the labeled data and accurately extract the relevant information from new documents.
  • Label sufficient data: The AI model needs a sufficient amount of labeled data to learn how to accurately extract the relevant information from documents. Make sure to label a sufficient number of documents to provide the AI model with enough examples to learn from.
  • Use a diverse set of documents: Use a diverse set of documents when labeling data to ensure that the AI model can accurately extract information from a range of document types and formats. This will help to ensure that the model is effective in extracting the relevant information from all types of documents.
  • Define the Quality Metrics for the labeled data:  Understand how quality can be measured for the labeled data in a specific use case.  This could be as simple as checking what data is labeled or it could involve identifying the effectiveness of the context in which data is labeled in the document (in case it occurs in multiple contexts).  There may also be a need to deal with duplicate occurrences of the same data within a document. Define the acceptable range across these parameters, that will enable training to be performed effectively.  
  • Continuously review and refine the labeling process: The labeling process should be reviewed and refined on an ongoing basis to ensure that the AI model is learning from accurate and relevant data. Regularly review the labeled data to identify any issues or inconsistencies, or adequacy of quality metrics gathered, and adjust the labeling guidelines as needed.
  • Use a tool designed for data labeling: There are many tools available for data labeling that are specifically designed to streamline the labeling process and improve accuracy. These tools can help to ensure that the labeling process is consistent, efficient, and effective.

Following these best practices for labeling data, will allow businesses to ensure that the AI model is learning from accurate and relevant data, which will help to improve the accuracy and efficiency of document extraction automation.

Next up, we look at mitigating risks with technology adoption in this segment, through an understanding of popular misconceptions that surround them. Like with any Formula 1 race, the car is half the battle and the other half is the road itself.  So we’ll scout out some of the usual pitfalls and cracks in the road ahead for these AI/ML solutions.

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