Image extraction followed up with text extraction from the image
Automating Insurance Card Image Extraction
The Akaike Edge
Key solutions built for the customer
Automated object detection for insurance card image 0%
Faster DL algorithm for information extraction 0%
Automated process for classifying the type of insurance 0%
Adjusted working process to adhere to HIPAA compliance
Before beginning to solve the problem, Health Insurance Portability and Accountability Act (HIPPA) compliance was to be respected. We then had to work remotely using virtual machines as per the client's environment. To adjust to this working process, a few labeling tools were built to maintain HIPAA compliance
Utilized object detection-based models to extract the insurance card from the image and LayoutLMV2 for information extraction.
We iterated with several segmentation and object detection-based algorithms to crop insurance cards from the given images, and then extract pertinent information from the insurance cards. Additionally, this insurance card extraction from the provided image was completed with a very low latency rate of 20 ms per image. Following that, LayoutLMV2, a transformer-based model for labelling the information such as Ids, Names, etc., received the extracted card image and text as input. Eventually, we improved the process' speed, dynamic nature, and accuracy by feeding the model additional relevant data. Finally, the classification model was used to this extracted insurance card image to determine the payer type.
Model deployment for object detection and information retrieval
The customized model was deployed with Rest API using FastAPI in the client’s AZURE environment using VMSS that automatically scales up and down the virtual machines as per the requirement. The whole process of insurance card extraction and information retrieval is now automatic, more accurate, and faster.
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