A Fair Evaluation of Various Deep Learning-Based Document Image Binarization Approaches

• URL: http://arxiv.org/abs/2401.11831v1
• Date: Mon, 22 Jan 2024 10:42:51 GMT
• Title: A Fair Evaluation of Various Deep Learning-Based Document Image Binarization Approaches
• Authors: Richin Sukesh, Mathias Seuret, Anguelos Nicolaou, Martin Mayr, Vincent Christlein
• Abstract summary: Binarization of document images is an important pre-processing step in the field of document analysis. Deep learning techniques are able to generate binarized versions of the images by learning context-dependent features. This work focuses on the evaluation of different deep learning-based methods under the same evaluation protocol.
• Score: 5.393847875065119
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Binarization of document images is an important pre-processing step in the field of document analysis. Traditional image binarization techniques usually rely on histograms or local statistics to identify a valid threshold to differentiate between different aspects of the image. Deep learning techniques are able to generate binarized versions of the images by learning context-dependent features that are less error-prone to degradation typically occurring in document images. In recent years, many deep learning-based methods have been developed for document binarization. But which one to choose? There have been no studies that compare these methods rigorously. Therefore, this work focuses on the evaluation of different deep learning-based methods under the same evaluation protocol. We evaluate them on different Document Image Binarization Contest (DIBCO) datasets and obtain very heterogeneous results. We show that the DE-GAN model was able to perform better compared to other models when evaluated on the DIBCO2013 dataset while DP-LinkNet performed best on the DIBCO2017 dataset. The 2-StageGAN performed best on the DIBCO2018 dataset while SauvolaNet outperformed the others on the DIBCO2019 challenge. Finally, we make the code, all models and evaluation publicly available (https://github.com/RichSu95/Document_Binarization_Collection) to ensure reproducibility and simplify future binarization evaluations.

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