Feature-extraction methods for historical manuscript dating based on writing style development

Dhali, Maruf A.; Jansen, Camilo Nathan; Wit, Jan Willem de; Schomaker, Lambert

doi:10.1016/j.patrec.2020.01.027

Cited by 35 publications

(17 citation statements)

References 13 publications

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“…The proposed method captures temporal information from iron-gall ink using the multispectral image technique combined with the kernel discriminant learning for an ordinal regression (KDLOR) classification approach. In another recent work [ 26 ], authors proposed using a grapheme-based method with the self-organizing time map (SOTM) as a codebook for dating the Dead Sea Scrolls collection.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Fusion Using Subsets of Multi-Features for Historical Arabic Manuscript Dating

2022

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Automatic dating tools for historical documents can greatly assist paleographers and save them time and effort. This paper describes a novel method for estimating the date of historical Arabic documents that employs hierarchical fusions of multiple features. A set of traditional features and features extracted by a residual network (ResNet) are fused in a hierarchical approach using joint sparse representation. To address noise during the fusion process, a new approach based on subsets of multiple features is being considered. Following that, supervised and unsupervised classifiers are used for classification. We show that using hierarchical fusion based on subsets of multiple features in the KERTAS dataset can produce promising results and significantly improve the results.

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Section: Related Workmentioning

confidence: 99%

Hierarchical Fusion Using Subsets of Multi-Features for Historical Arabic Manuscript Dating

2022

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“…Wolf et al [ 18 ] explored handwriting matching and paleographic classification, focusing on the documents from the Cairo Genizah collection. Dhali et al [ 19 ] used textural and grapheme-based features with support vector regression to determine the date of ancient texts from the Dead Sea Scrolls collection. Ben Ezra et al [ 20 ] trained a model for establishing the reading order of the main text by detecting insertion markers that indicate marginal additions.…”

Section: Related Workmentioning

confidence: 99%

Digital Hebrew Paleography: Script Types and Modes

et al. 2022

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Paleography is the study of ancient and medieval handwriting. It is essential for understanding, authenticating, and dating historical texts. Across many archives and libraries, many handwritten manuscripts are yet to be classified. Human experts can process a limited number of manuscripts; therefore, there is a need for an automatic tool for script type classification. In this study, we utilize a deep-learning methodology to classify medieval Hebrew manuscripts into 14 classes based on their script style and mode. Hebrew paleography recognizes six regional styles and three graphical modes of scripts. We experiment with several input image representations and network architectures to determine the appropriate ones and explore several approaches for script classification. We obtained the highest accuracy using hierarchical classification approach. At the first level, the regional style of the script is classified. Then, the patch is passed to the corresponding model at the second level to determine the graphical mode. In addition, we explore the use of soft labels to define a value we call squareness value that indicates the squareness/cursiveness of the script. We show how the graphical mode labels can be redefined using the squareness value. This redefinition increases the classification accuracy significantly. Finally, we show that the automatic classification is on-par with a human expert paleographer.

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“…. ) [7,6]. Hybrid methods combining multimodal features like appearance-based features and textual labels not only from the image itself but from its context when it appears in a document like a newspaper or a web page [19] have also demonstrated interesting performance.…”

Section: Introductionmentioning

confidence: 99%

A Generic Image Retrieval Method for Date Estimation of Historical Document Collections

Molina¹,

Gómez²,

Terrades³

et al. 2022

Preprint

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Date estimation of historical document images is a challenging problem, with several contributions in the literature that lack of the ability to generalize from one dataset to others. This paper presents a robust date estimation system based in a retrieval approach that generalizes well in front of heterogeneous collections. we use a ranking loss function named smooth-nDCG to train a Convolutional Neural Network that learns an ordination of documents for each problem. One of the main usages of the presented approach is as a tool for historical contextual retrieval. It means that scholars could perform comparative analysis of historical images from big datasets in terms of the period where they were produced. We provide experimental evaluation on different types of documents from real datasets of manuscript and newspaper images.

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Feature-extraction methods for historical manuscript dating based on writing style development

Cited by 35 publications

References 13 publications

Hierarchical Fusion Using Subsets of Multi-Features for Historical Arabic Manuscript Dating

Hierarchical Fusion Using Subsets of Multi-Features for Historical Arabic Manuscript Dating

Digital Hebrew Paleography: Script Types and Modes

A Generic Image Retrieval Method for Date Estimation of Historical Document Collections

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