Assessing the impact of Binarization for Writer Identification in Greek Papyrus

TL;DR

This study evaluates how different binarization techniques affect writer identification accuracy in Greek papyri, emphasizing the importance of binarization quality and data augmentation in historical document analysis.

Contribution

It systematically compares traditional and deep learning binarization methods and analyzes their impact on writer identification in Greek papyri, highlighting the role of data augmentation.

Findings

Deep learning binarization improves writer identification accuracy.

Binarization quality strongly correlates with identification performance.

Data augmentation enhances deep learning model effectiveness.

Abstract

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 This is challenging in historical documents, in our case Greek papyri, as background is often non-uniform, fragmented, and discolored with visible fiber structures.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.