Description:
convenient storage and use of information obtained from medical tests of clients of a mobile telemedicine application involves a friendly process of downloading and digitizing paper medical tests. Automation of loading and systematization of sets of medical analyzes for the user of the mobile platform uses modern methods of detecting and classifying images for their subsequent processing.
Context:
For users of a mobile medical application, it is necessary to implement a model that will allow the user to upload an image and automatically tag it
Solution:
a model was created based on OCR and a language recognition model
Results:
Solutions for recognition automation have been built:
To build the model we used:
Simulation results:
A model for classifying images into categories.
Customer: Medicine, Telecom
Technology stack: OCR, TensorFlow, nltk, Python.
convenient storage and use of information obtained from medical tests of clients of a mobile telemedicine application involves a friendly process of downloading and digitizing paper medical tests. Automation of loading and systematization of sets of medical analyzes for the user of the mobile platform uses modern methods of detecting and classifying images for their subsequent processing.
Context:
For users of a mobile medical application, it is necessary to implement a model that will allow the user to upload an image and automatically tag it
Solution:
a model was created based on OCR and a language recognition model
Results:
- The solution is integrated into the customer’s business process:
- Reducing the workload on specialists by 60%;
- Increase in the speed of document processing in a business process by 10%;
- Reduced costs due to lost documents by 5%.
Solutions for recognition automation have been built:
- Checks and receipts;
- Invoice;
- Work order;
- Contracts.
To build the model we used:
- A set of categories of medical tests;
- Database of medical analysis images;
- Tagging images into categories.
Simulation results:
A model for classifying images into categories.
Customer: Medicine, Telecom
Technology stack: OCR, TensorFlow, nltk, Python.
