OCR-PostProcessing
| dc.contributor.author | Haiden, Simon | |
| dc.contributor.author | Heindl, Andreas | |
| dc.contributor.author | Hofer, Lukas | |
| dc.contributor.author | Primetshofer, Julian | |
| dc.date.accessioned | 2024-04-03T17:23:53Z | |
| dc.date.available | 2024-04-03T17:23:53Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | The OCR post-processing project involves the improvement of text generated by text recognition. The goal is to achieve the best possible improvements using various approaches. For implementation four components are required: The backend, which tries to enhance the given texts using three different approaches, including two dictionary methods and one AI-based improvement. Additionally, the backend calculates statistics to assess the performance of each system. Improvements are tested and visualized through the frontend, which allows for the uploading of multiple files or entire datasets at once to be improved with all desired correction systems. All changes are displayed after the improvement process is completed and the backend-calculated statistics are visualized. An API connects the user interface and the backend. In Python, using Flask, several endpoints were defined to facilitate the exchange of information between the frontend and the backend. Furthermore, a test pipeline allows for the improvement to be used and tested without a frontend. This pipeline can process a predefined folder structure, correcting all files contained and comparing them with the ground truth. The result is a comprehensive application composed of Python modules, JavaScript code, and PowerShell scripts. | |
| dc.identifier.uri | https://dspace.htl-perg.ac.at/handle/htl-perg/1353 | |
| dc.title | OCR-PostProcessing | |
| htl.speciality | Informatik |
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