Assessing the impact of Binarization for Writer Identification in Greek Papyrus (2506.15852v1)

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 This is challenging in historical documents, in our case Greek papyri, as background is often non-uniform, fragmented, and discolored with visible fiber structures.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

Assessing the Impact of Binarization for Writer Identification in Greek Papyrus

The paper "Assessing the Impact of Binarization for Writer Identification in Greek Papyrus" by Dominic Akt, Marco Peer, and Florian Kleber explores the role of image binarization in improving writer identification performance on degraded historical documents like Greek papyri. Writer identification is critical in document analysis, with applications in both historical analysis and forensic science. The paper scrutinizes the effectiveness of deep learning (DL) techniques in binarization compared to traditional methods, highlighting performance on subsequent writer identification tasks.

Methodology Overview

The authors examine writer identification using a two-pronged approach— writer retrieval and writer classification— to distinguish pieces of text authored by the same writer. A substantial issue in automated writer identification, particularly with historical documents such as Greek papyrus, arises from the document's condition, which typically includes non-uniform discoloration and structural degradation due to age.

This paper deploys a diverse set of DL models and traditional binarization methods to test on the DIBCO 2019 dataset, particularly focusing on papyri. The models evaluated include DP-LinkNet, DeepOtsu, NAF-DPM, and Robin, among others, which undergo training with and without custom data augmentation techniques to adapt to papyrus' unique textural characteristics.

Key Findings

1. Data Augmentation Benefits:
   • DL models, notably DP-LinkNet and NAF-DPM, demonstrate substantial performance improvement (up to 19 percentage points in FM) with the incorporation of data augmentation techniques. These techniques simulate papyrus texture and degradation features in the training data, crucial for model adaptation to the papyrus characteristics.
2. Traditional vs. DL Approaches:
   • Traditional methods like Otsu's and Sauvola's performed poorly on papyri, reinforcing the superior adaptability of DL methods which leverage context-dependent learning capabilities. The degradation and non-uniform backgrounds in papyrus confound histogram-based methods.
3. Model Selection Metrics:
   • The FM metric emerged as an optimal model selection criterion during training, yielding higher writer identification and retrieval performance compared to PSNR and pseudo-FM criteria.
4. Correlation Analysis:
   • The paper found a strong positive correlation between papyrus binarization performance (as measured by PSNR on Set B of DIBCO 2019) and subsequent writer identification efficacy, suggesting PSNR as a reliable indicator for model performance selection in contexts involving degraded historical texts.

Implications

The research highlights the critical role of effective binarization in historical document analysis—a preprocessing step that significantly influences the outcome of writer classification and retrieval algorithms. Improved binarization, facilitated by advanced DL models and data augmentation strategies, contributes meaningfully to the reconstruction and analysis of historical texts, enhancing paleographic research and potentially assisting forensic investigations related to document authorship.

Speculations for Future Developments

With advancements in DL models, particularly those emphasizing context-awareness and feature learning from degraded images, the future of document analysis, especially that of historical texts, appears promising. The application of robust AI techniques will likely extend to a broader set of document types, potentially improving computational accuracy in paleography and even expanding into automated reconstruction of fragmented documentation.

This paper sets a foundation for enhanced feature extraction methodologies in complex document analysis tasks. Further research may explore larger datasets and more sophisticated augmentation strategies, improving model generalizability to diverse historical contexts beyond Greek papyri. The integration of semi-supervised learning approaches could also furnish further enhancements in the fine-tuning of models on limited, specialized datasets typical in historical document research.