OCR and datamine of medical records

This case study highlights our work with a industry expert in healthcare to streamline their medical document processing system. Faced with the challenge of managing a huge number of documents including PDFs, images, and Excel sheets filled with essential medical data, the provider needed a solution that could accurately extract structured information such as dates, descriptions, charges, and specific medical codes.


To address these challenges, we developed a tailored backend service capable of processing these diverse document types efficiently. Our solution involved a comprehensive analysis of each document to detect and extract key features such as lines, tables, shaded areas, barcodes, handwritten segments, and headings. For documents that did not contain accessible text, our approach utilized Optical Character Recognition (OCR) technology, integrating multiple engines to enhance the accuracy of text recognition. This was particularly crucial for ensuring that all data, regardless of its format or the quality of its source document, was accurately captured and converted into a usable digital format. Beyond simple text extraction, our solution incorporated specialized data mining techniques. These were designed to organize the extracted information into structured formats, tailored to the healthcare provider's specific needs for further analysis and application.


The implementation of our backend service made it possible for the provider to efficiently and accurately process medical records . Key outcomes included:

  • Enhanced accuracy in data extraction, reducing errors in patient records and billing.
  • Faster processing times, enabling quicker access to critical medical information.
  • Reduced manual labor costs associated with document handling and data entry.
  • Improved data organization, making it easier for the provider to analyze and utilize the information.


This case study demonstrates our ability to deliver customized backend solutions that address the specific challenges faced by our clients. By leveraging advanced OCR technology and data mining techniques, we helped the healthcare provider streamline their document processing system, resulting in improved efficiency, accuracy, and cost savings.

Case Study: Enhancing AI and OCR Engine Performance for Serverless Environments

In this case study, we explore our initiative to optimize proprietary AI models and OCR (Optical Character Recognition) engines, focusing on handwritten detection and text spotting. Our objective was to achieve high efficiency in processing documents, particularly in environments with limited hardware resources, such as those lacking GPUs, and with minimal CPU cores and RAM.


Our team embarked on a project to refine our AI models, ensuring they could accurately detect handwritten text and spot specific texts within documents under challenging conditions. A significant part of this project involved adapting our OCR engines to operate effectively on resource-constrained hardware, a necessity for our move towards serverless computing environments.

The optimization process was twofold:
  • AI Model Enhancement: We fine-tuned our AI algorithms to improve their ability to recognize handwriting and text with greater accuracy and speed, ensuring they could handle a wide variety of document types and conditions.
  • OCR Engine Adaptation: We developed a method to deploy multiple OCR engines in parallel, optimizing their performance to work within environments with limited computing resources.

These advancements were critical for our transition to a serverless architecture, where computing resources are more restricted, and there is a need to deliver results within tight deadlines.


The optimization of our AI models and OCR engines led to several significant improvements:

  • Efficient Performance on Limited Resources: Our system can now operate in hardware-constrained environments without sacrificing speed or accuracy, making it ideal for serverless computing.
  • Scalability and Cost-Effectiveness: By adapting our technology for serverless use, we can process vast quantities of documents with reduced hardware requirements, leading to lower operational costs.
  • Enhanced Handwriting Detection and Text Spotting: The accuracy and speed of processing documents containing handwritten notes or specific text elements have significantly increased.


This case study demonstrates our commitment to innovation and efficiency in document processing technology. By optimizing our AI models and OCR engines for serverless environments, we have enabled our systems to deliver fast, accurate document processing solutions that are both scalable and cost-effective, even in settings with limited computing resources.