Touching Component Extraction and Separation in Arabic Manuscripts

This paper addresses the problem of touching characters (TC) extraction and separation in Arabic manuscripts. It proposes a recognition based method to separate them and join each component piece to its corresponding word. The proposed method extracts the TCs in the document whether between successive text-lines or words of the same text-line. For vertically/up-down TCs, we improved an existing method. For horizontally /left-right TCs, we proposed a novel extraction method based on the morphology analysis of the terminal letters of Arabic words. Then, it recognizes the TCs relying on templates, using shape context descriptor and an interpolation function the TPS transformation (Thin Plate Spline). Finally, it segments them based on the distance from the central points (midpoints or gravity centers) of the recognized template’s parts. Tests are performed using a large dataset of TCs and three metrics: Manhattan, Euclidean and Canberra distances. Obtained results strongly support the efficiency of the proposed TC extraction and segmentation methods and outperform results of some related works, taking into account the different types, variability and complexity of the TCs.

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Publication Details

Journal
ELCVIA Electronic Letters on Computer Vision and Image Analysis
Published
2026-03-02
DOI
https://doi.org/10.5565/rev/elcvia.2019
Primary Topic
Handwritten Text Recognition Techniques
Type
article
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article

Touching Component Extraction and Separation in Arabic Manuscripts

AOUADI Nabil
ELCVIA Electronic Letters on Computer Vision and Image Analysis
Handwritten Text Recognition Techniques
article

Touching Component Extraction and Separation in Arabic Manuscripts

AOUADI Nabil
article en

Abstract

This paper addresses the problem of touching characters (TC) extraction and separation in Arabic manuscripts. It proposes a recognition based method to separate them and join each component piece to its corresponding word. The proposed method extracts the TCs in the document whether between successive text-lines or words of the same text-line. For vertically/up-down TCs, we improved an existing method. For horizontally /left-right TCs, we proposed a novel extraction method based on the morphology analysis of the terminal letters of Arabic words. Then, it recognizes the TCs relying on templates, using shape context descriptor and an interpolation function the TPS transformation (Thin Plate Spline). Finally, it segments them based on the distance from the central points (midpoints or gravity centers) of the recognized template’s parts. Tests are performed using a large dataset of TCs and three metrics: Manhattan, Euclidean and Canberra distances. Obtained results strongly support the efficiency of the proposed TC extraction and segmentation methods and outperform results of some related works, taking into account the different types, variability and complexity of the TCs.

ELCVIA Electronic Letters on Computer Vision and Image AnalysisVol. 24(2)
LatinoJustice PRLDEF (US)
Quality Education
Openalex Percentile: Top 38%
Handwritten Text Recognition Techniques
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