I'm currently working on a project that involves using XtractEdge (which is similar to Amazon Textract, Microsoft Intelligent Document Processing) to extract valuable information from a variety of documents. One of the major challenges I'm facing is how to effectively validate these extractions without manual intervention. I'd greatly appreciate your insights and answers into different techniques that could be employed for this validation process.
I'm particularly interested in exploring the following approaches:
Consistency or Rule-Based Validation: How can I develop a reliable rule-based validation system to ensure the consistency of extracted information? Are there any best practices or tools you recommend for setting up such a system?
Utilizing Algorithms or Statistical Methods: I'm curious about integrating algorithms or statistical methods to validate the accuracy of the extracted data. What are some commonly used techniques in this area, and how can they be applied to the context of information extraction?
Cross Verification with Other Sources: Cross verification with external sources seems promising. Could you share your experiences with implementing this approach? What are the potential challenges and benefits of comparing extracted information with data from other trusted sources?
If you've encountered similar challenges or have expertise in information extraction and validation, I would greatly appreciate any guidance or suggestions you can provide.
Thank you in advance for your time and assistance!
The idea which is revolving in my mind is to use the results extracted from any Intelligent Document Processing application (IDP) and use a Document Question and Answering Transformers from HuggingFace to compare the results from both the applications and validate the outputs obtained in each case. But the point where I face a hurdle is, to find the right IDP and implement a transformer in it to validate the outputs.
