To Combine or Not to Combine? The Influence of Combining Training Datasets on the Robustness of Deep Learning Models: An Analysis for Optical Character Recognition of Handwriting

The present manuscript addresses the question of how training data should be sampled for deep learning models by analyzing and evaluating the impact of training data representation and complexity on the performance and robustness of deep learning models. To address this open question, we take a comb...

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Detaylı Bibliyografya
Asıl Yazarlar:	Leopold Fischer-Brandies, Lucas Muller, Benjamin Rebholz, Ricardo Buettner
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	IEEE 2025-01-01
Seri Bilgileri:	IEEE Access
Konular:	Robustness training data deep learning transfer learning OCR handwriting
Online Erişim:	https://ieeexplore.ieee.org/document/10946162/
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To Combine or Not to Combine? The Influence of Combining Training Datasets on the Robustness of Deep Learning Models: An Analysis for Optical Character Recognition of Handwriting

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