A Building-Block Approach to Character-Level Writer Verification on the Great Isaiah Scrolls.

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Bibliographic Details
Title:	A Building-Block Approach to Character-Level Writer Verification on the Great Isaiah Scrolls.
Authors:	Tobing, T. Lumban¹ tabita.tobing@ntnu.no, Bours, P.¹
Source:	Journal of Imaging Science & Technology. Mar/Apr2025, Vol. 69 Issue 2, p1-10. 10p.
Subjects:	Graphology, Plurality voting, Historical source material, Machine learning, Historical analysis
Abstract:	This study presents a novel character-level writer verification framework for ancient manuscripts, employing a building-block approach that integrates decision strategies across multiple token levels, including characters, words, and sentences. The proposed system utilized edge-directional and hinge features along with machine learning techniques to verify the hands that wrote the Great Isaiah Scroll. A custom dataset containing over 12,000 samples of handwritten characters from the associated scribes was used for training and testing. The framework incorporated character-specific parameter tuning, resulting in 22 separate models and demonstrated that each character has distinct features that enhance system performance. Evaluation was conducted through soft voting, comparing probability scores across different token levels, and contrasting the results with majority voting. This approach provides a detailed method for multi-scribe verification, bridging computational and paleographic methods for historical manuscript studies. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	This study presents a novel character-level writer verification framework for ancient manuscripts, employing a building-block approach that integrates decision strategies across multiple token levels, including characters, words, and sentences. The proposed system utilized edge-directional and hinge features along with machine learning techniques to verify the hands that wrote the Great Isaiah Scroll. A custom dataset containing over 12,000 samples of handwritten characters from the associated scribes was used for training and testing. The framework incorporated character-specific parameter tuning, resulting in 22 separate models and demonstrated that each character has distinct features that enhance system performance. Evaluation was conducted through soft voting, comparing probability scores across different token levels, and contrasting the results with majority voting. This approach provides a detailed method for multi-scribe verification, bridging computational and paleographic methods for historical manuscript studies. [ABSTRACT FROM AUTHOR]
ISSN:	10623701
DOI:	10.2352/J.ImagingSci.Technol.2025.69.2.020401