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Improving the accuracy of optical character recognition of stone engravings using image pre-processing methods

This study focuses on the development of preprocessing methods to improve the accuracy of Optical Character Recognition (OCR) for stone engravings. The primary goal is to enhance the precision of widely used OCR tools, particularly for texts engraved on stone surfaces, which present unique challenges that differ from traditional OCR applications. Emphasis is placed on developing image preprocessing methods as a software product. Customized image manipulation scripts were used to improve recognition accuracy and address issues such as contrast, alignment, noise, and resolution. The preprocessing stage was integrated into the workflow designed for image transformation before OCR processing. Subsequently, the recognition improvements were evaluated based on text similarity metrics analysis. Iterative text recognition and repeated recognition of images after applying preprocessing demonstrated significant improvements in OCR accuracy. This work provides a solid foundation for further enhancement of OCR workflows by employing adaptable preprocessing techniques specifically designed for particular problem areas, achieving higher precision in text recognition.

Author: Romans Urbans-Orbans

Supervisor: Aleksandrs Grakovskis

Degree: Bachelor

Year: 2024

Work Language: Latvian

Study programme: Computer Science

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CNN-Based pipeline-related artifact and damage recognition in IHC staining as preprocessing step for pathological analysis

This work proposes automated solution for artifact and damage segmentation in biomedical images using machine learning algorithms. The development process includes data preprocessing, label classification using a clustering algorithm and segmentation model. CNN architectures like YOLO and U-NET are utilized for segmentation, and K-Means and DBSC algorithms are evaluated for clustering. The outcomes include a set of data preprocessing precodures, clustering algorithm testing and results analysis, segmentation model and recommendations for further development.

Author: Taisija Kožarina

Supervisor: Jeļena Kijonoka

Degree: Bachelor

Year: 2024

Work Language: English

Study programme: Computer Science

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