Home › 
Use Cases › 
Analysis of handwritten documents

Use Case

Analysis of handwritten documents

Handwritten documents can be read out and prefilled using machine learning algorithms. By further input, these documents can be edited again, which again benefits the accuracy of the algorithm.



In many industries, documents are filled out in handwriting by own employees or partner companies. These handwritten documents often have to be typed in order to process them further.



With Machine Learning algorithms, patterns in handwritten documents can be analyzed. The more documents are available and the more uniformly the documents are filled out, the better the algorithm recognizes the contents of the handwritten document.



The pre-filled documents are transferred to an interface and can be edited again by employees. The algorithm learns new patterns and can apply them to new cases.

More Use Cases

Electronic patient records can be evaluated using machine learning algorithms. This automatically provides doctors with suggestions for diagnoses and therapy options.

Diagnostic suggestions can be made by analyzing X-ray or MRI images using an algorithm.

Companies working with thousands of customers and suppliers need to categorize their documents so that requests can be handled in time.

Authorities need up-to-date information on the use of their land to be able to plan public infrastructure.

Find out what dida can do for you