Writer Identification for Historical Arabic Documents

Fecker, Daniel; Asit, Abedelkadir; Märgner, Volker; El-Sana, Jihad; Fingscheidt, Tim

doi:10.1109/icpr.2014.526

Cited by 22 publications

(7 citation statements)

References 14 publications

(25 reference statements)

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“…Instead, within the context of historical texts, computerized writer identification relies on annotation of epigraphers or paleographers-specialists on ancient writing systems. Examples of such studies cover topics as diverse as ancient Greek inscriptions [22]; Byzantine and Spanish Medieval codices [23,24]; Herman Melville's alleged 19 th c. texts [25]; 13 th -20 th c. Arabic and Turkish manuscripts [26][27][28]; as well as Hebrew Second Temple [29] and Medieval [30,31] documents. This means that an often subjective opinion of "manuscript historians" https://doi.org/10.1371/journal.pone.0237962.g002…”

Section: Plos Onementioning

confidence: 99%

“…A review of [22][23][24][25][26][27][28][29][30][31] and other computerized writer identification surveys [33,34] reveals another potential problem. Commonly, the employed algorithms utilize computer vision and machine learning features and procedures to produce abstract "distances" between inscriptions-e.g., based on the slant of their characters, their relative proportions, their uniformity, etc.…”

Section: Plos Onementioning

confidence: 99%

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Forensic document examination and algorithmic handwriting analysis of Judahite biblical period inscriptions reveal significant literacy level

Shaus

Gerber²,

Faigenbaum-Golovin

et al. 2020

PLoS ONE

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Arad is a well preserved desert fort on the southern frontier of the biblical kingdom of Judah. Excavation of the site yielded over 100 Hebrew ostraca (ink inscriptions on potsherds) dated to ca. 600 BCE, the eve of Nebuchadnezzar's destruction of Jerusalem. Due to the site's isolation, small size and texts that were written in a short time span, the Arad corpus holds important keys to understanding dissemination of literacy in Judah. Here we present the handwriting analysis of 18 Arad inscriptions, including more than 150 pair-wise assessments of writer's identity. The examination was performed by two new algorithmic handwriting analysis methods and independently by a professional forensic document examiner. To the best of our knowledge, no such large-scale pair-wise assessments of ancient documents by a forensic expert has previously been published. Comparison of forensic examination with algorithmic analysis is also unique. Our study demonstrates substantial agreement between the results of these independent methods of investigation. Remarkably, the forensic examination reveals a high probability of at least 12 writers within the analyzed corpus. This is a major increment over the previously published algorithmic estimations, which revealed 4-7 writers for the same assemblage. The high literacy rate detected within the small Arad stronghold, estimated (using broadly-accepted paleo-demographic coefficients) to have accommodated 20-30 soldiers, demonstrates widespread literacy in the late 7 th century BCE Judahite military and administration apparatuses, with the ability to compose biblical texts during this period a possible by-product.

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Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Forensic document examination and algorithmic handwriting analysis of Judahite biblical period inscriptions reveal significant literacy level

Shaus

Gerber²,

Faigenbaum-Golovin

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Second, the imperfect digital images present a challenge for image segmentation and enhancement methods [25,26]. Third, although the task of identifying writers in handwritten texts has been addressed in previous literature (e.g., [27][28][29][30][31][32]), researchers presuppose a reference dataset with annotated authorship be used for training purposes, which is not present in our case. Additionally, the above publications do not aim directly at recognizing or distinguishing authors.…”

Section: Algorithmic Apparatusmentioning

confidence: 99%

Algorithmic handwriting analysis of the Samaria inscriptions illuminates bureaucratic apparatus in biblical Israel

et al. 2020

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Past excavations in Samaria, capital of biblical Israel, yielded a corpus of Hebrew ink on clay inscriptions (ostraca) that documents wine and oil shipments to the palace from surrounding localities. Many questions regarding these early 8 th century BCE texts, in particular the location of their composition, have been debated. Authorship in countryside villages or estates would attest to widespread literacy in a relatively early phase of ancient Israel's history. Here we report an algorithmic investigation of 31 of the inscriptions. Our study establishes that they were most likely written by two scribes who recorded the shipments in Samaria. We achieved our results through a method comprised of image processing and newly developed statistical learning techniques. These outcomes contrast with our previous results, which indicated widespread literacy in the kingdom of Judah a century and half to two centuries later, ca. 600 BCE. Competing interests:The authors have declared that no competing interests exist. Fig 2. Map marking places and clans mentioned in the Samaria ostraca; based on [5,10]. In capital red are the names of the Manasseh tribal clans; in black are the names of identifiable localities.

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“…Apart from other methods [20][21][22][23][24], deep learning is another approach which can be used for writer identification. Convolutional neural network (CNN) is a one of the deep learning models that can be employed.…”

Section: Vinita Balbhim Patil Rajendra R Patilmentioning

confidence: 99%

Writer Identification with Hybrid Model using Hybrid HMM and ANN

Patil*¹,

Patil²

2019

IJRTE

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In this paper, writer identification is performed with three models, namely, HMMBW, HMMMLP and HMMCNN. The features are extracted from the HMM and are classified using Baum Welch algorithm (BW), Multi layer perceptron (MLP) model and Convolutional neural network (CNN) model. A dataset, namely, VTU-WRITER dataset is created for the experiential purpose and the performance of the models were tested. The test train ratio was varied to derive its relation to accuracy. Also the number of states was varied to determine the optimum number of states to be considered in the HMM model. Finally the performance of all the three models is compared

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Writer Identification for Historical Arabic Documents

Cited by 22 publications

References 14 publications

Forensic document examination and algorithmic handwriting analysis of Judahite biblical period inscriptions reveal significant literacy level

Forensic document examination and algorithmic handwriting analysis of Judahite biblical period inscriptions reveal significant literacy level

Algorithmic handwriting analysis of the Samaria inscriptions illuminates bureaucratic apparatus in biblical Israel

Writer Identification with Hybrid Model using Hybrid HMM and ANN

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