\chapter{Abstract}
\begin{english}

This bachelor's thesis focuses on one of the many fields of optical character recognition: the extraction of textual data from digital screenshots. The goal is to maximize the amount and quality of the resulting data, hence simplifying the management of graphical resources for COPA-DATA's product documentation. Additionally, a contribution to research in the field of text recognition in graphical user interfaces is made.

For this purpose, a selection of algorithms for image processing and filtering of the resulting data is made. After explaining the selected algorithms in their basic function, they are objectively compared using common metrics for speech and text recognition.

By comparing the resulting data in the automatically generated report, it becomes apparent that analyzing different pictures using just one method does not lead to optimal results. Amongst the chosen approaches, there is no clear "winner" in the examined sample. In order to not lose any important details, specifically when working with binarization techniques such as thresholding, the chosen parameters must match the individual characteristics of the picture. Therefore, for automated text recognition, it is sensible to either manually determine the algorithm to use based on the input data or to automatically combine the results of multiple algorithms. This way, as many details as possible can be captured and the end results of text recognition within the image can be optimized.

For further research or adaptation to specific requirements, the prototypical implementation or its respective components are designed to be reused. Due to the modular structure of the automated comparison system, it is possible to add new functionality or edit the existing features. As a result, the ideal text recognition approach can always be determined with little effort, even after changing the display language or after a complete redesign of the graphical user interface.
\end{english}