对比二值化对古希腊纸草文书作者识别的影响，发现质量越高效果越好。
Assessing the impact of Binarization for Writer Identification in Greek Papyrus
- 用深度学习模型改进古籍二值化，提升背景去除精度
- 数据增强显著提升深度学习模型的二值化性能
- 二值化质量与作者识别准确率强相关，适合历史文献研究者
本文研究古希腊纸草文书的作者识别任务。传统作者识别流程中常采用图像二值化预处理，以避免模型学习背景特征。然而，历史文献如古希腊纸草常存在非均匀、碎片化且泛黄的背景，伴有明显纤维结构，使二值化困难。本文对比了传统二值化方法与前沿深度学习（DL）模型，并评估二值化质量对后续作者识别性能的影响。深度学习模型在训练时使用自定义数据增强技术，并采用不同模型选择标准。系统性评估在DIBCO 2019数据集上进行。结果表明，数据增强能显著提升深层模型表现；同时，二值化在DIBCO 2019上的有效性与下游作者识别性能呈强相关性。
原文摘要 · Abstract (English)
This paper tackles the task of writer identification for Greek papyri. A common preprocessing step in writer identification pipelines is image binarization, which prevents the model from learning background features. This is challenging in historical documents, in our case Greek papyri, as background is often non-uniform, fragmented, and discolored with visible fiber structures. We compare traditional binarization methods to state-of-the-art Deep Learning (DL) models, evaluating the impact of binarization quality on subsequent writer identification performance. DL models are trained with and without a custom data augmentation technique, as well as different model selection criteria are applied. The performance of these binarization methods, is then systematically evaluated on the DIBCO 2019 dataset. The impact of binarization on writer identification is subsequently evaluated using a state-of-the-art approach for writer identification. The results of this analysis highlight the influence of data augmentation for DL methods. Furthermore, findings indicate a strong correlation between binarization effectiveness on papyri documents of DIBCO 2019 and downstream writer identification performance.
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