ReCoptic: Computer Vision for the Reconstruction of Dismembered Coptic Codices

In the course of history, many ancient codices (i.e., bound volumes of manuscripts) written in the Coptic language have been dismembered, often at the hand of sellers of antiques, into individual sheets, who have ended up scattered across the planet. Reconstructing these codices in their original form would be extremely important for a better understanding of the culture of Coptic-speaking communities, and is a long-standing goal of paleographers and egyptologists alike. In this paper we present ReCoptic, a probabilistic, "contrastive" image classification system based on computer vision techniques, whose goal is to aid scholars in reconstructing dismembered ancient Coptic codices. Given a collection of scans of individual pages of ancient Coptic manuscripts, the system evaluates, for each pair of such scans, the ("posterior") probability that the two pages originate from the same codex, and ranks all such pairs in descending order of their associated posterior probability. The scholar can thus discover yet unknown pairs of pages originating from the same codex by examining, starting from the top of the list, the pairs proposed by ReCoptic. In experiments that we have run on a collection of 6,000+ pages of Coptic manuscripts, ReCoptic displays extremely high accuracy. The code for reproducing these experiments is available at https://github.com/lorebianchi98/ReCoptic

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Published
2025-09-08
DOI
https://doi.org/10.1109/ieee-ch65308.2025.11279398
Primary Topic
Handwritten Text Recognition Techniques
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article
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article

ReCoptic: Computer Vision for the Reconstruction of Dismembered Coptic Codices

Costanza Bianchi, Fabrizio Sebastiani, Lorenzo Bianchi, Alejandro Moreo
Handwritten Text Recognition Techniques
article

ReCoptic: Computer Vision for the Reconstruction of Dismembered Coptic Codices

Costanza Bianchi, Fabrizio Sebastiani, Lorenzo Bianchi, Alejandro Moreo
article en

Abstract

In the course of history, many ancient codices (i.e., bound volumes of manuscripts) written in the Coptic language have been dismembered, often at the hand of sellers of antiques, into individual sheets, who have ended up scattered across the planet. Reconstructing these codices in their original form would be extremely important for a better understanding of the culture of Coptic-speaking communities, and is a long-standing goal of paleographers and egyptologists alike. In this paper we present ReCoptic, a probabilistic, "contrastive" image classification system based on computer vision techniques, whose goal is to aid scholars in reconstructing dismembered ancient Coptic codices. Given a collection of scans of individual pages of ancient Coptic manuscripts, the system evaluates, for each pair of such scans, the ("posterior") probability that the two pages originate from the same codex, and ranks all such pairs in descending order of their associated posterior probability. The scholar can thus discover yet unknown pairs of pages originating from the same codex by examining, starting from the top of the list, the pairs proposed by ReCoptic. In experiments that we have run on a collection of 6,000+ pages of Coptic manuscripts, ReCoptic displays extremely high accuracy. The code for reproducing these experiments is available at https://github.com/lorebianchi98/ReCoptic

University of Modena and Reggio Emilia (IT), Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" (IT)
Openalex Percentile: Top 31%
Handwritten Text Recognition Techniques
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