Evaluation of Ensemble Learning Techniques for handwritten OCR Improvement

Martin Preiß

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arXiv · 2509.16221

Evaluation of Ensemble Learning Techniques for handwritten OCR Improvement

Martin Preiß

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Abstract

For the bachelor project 2021 of Professor Lippert's research group, handwritten entries of historical patient records needed to be digitized using Optical Character Recognition (OCR) methods. Since the data will be used in the future, a high degree of accuracy is naturally required. Especially in the medical field this has even more importance. Ensemble Learning is a method that combines several machine learning models and is claimed to be able to achieve an increased accuracy for existing methods. For this reason, Ensemble Learning in combination with OCR is investigated in this work in order to create added value for the digitization of the patient records. It was possible to discover that ensemble learning can lead to an increased accuracy for OCR, which methods were able to achieve this and that the size of the training data set did not play a role here.

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BibTeX RIS

Martin Preiß. 2025-09-11. Evaluation of Ensemble Learning Techniques for handwritten OCR Improvement. https://arxiv.org/abs/2509.16221

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