Segmentation of Ancient Arabic Documents

Belaïd, Abdel; Ouwayed, Nazih

doi:10.1007/978-1-4471-4072-6_5

Cited by 11 publications

(5 citation statements)

References 36 publications

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“…However, there were subpar outcomes in character segmentation, stemming from challenges related to complexity. [33] 2023 Baseline segmentation (BS)…”

Section: 500 Words Of Arabic Handwritten Samplesmentioning

confidence: 99%

Segmentation techniques for Arabic handwritten: a review

Abdalla Sheikh,

Sanusi Azmi,

Abdel Karim Abuain

et al. 2024

IJECE

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Image segmentation refers to the process of partitioning a page into distinct sections. This technique aims to improve and transform the image's representation into a more coherent and user-friendly format. Its common application involves identifying objects and boundaries (such as lines and curves) within images. However, this paper focuses on discussing segmentation methods specifically tailored for Arabic handwritten content. Dealing with the segmentation of Arabic handwritten material poses a significant challenge due to the diverse handwriting styles and the interconnection between Arabic letters. The paper will also touch on the classification of segmentation algorithms originally designed for modern documents, illustrating their adaptation in document processing. Furthermore, the paper will address the difficulties associated with segmenting Arabic handwritten content, including variations in writing style, the connected nature of Arabic characters, the complexity of Arabic cursive writing and as well as the diacritics challenges. Lastly, a concise overview of previously widely used segmentation techniques in various research endeavors will be provided.

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“…However, there were subpar outcomes in character segmentation, stemming from challenges related to complexity. [33] 2023 Baseline segmentation (BS)…”

Section: 500 Words Of Arabic Handwritten Samplesmentioning

confidence: 99%

Segmentation techniques for Arabic handwritten: a review

Abdalla Sheikh,

Sanusi Azmi,

Abdel Karim Abuain

et al. 2024

IJECE

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“…In addition, a pre-processing phase is necessary to remove background noise (superfluous information appearing from the verso) and non-textual regions. Belaïd and Ouwayed proposed a multi-oriented text line extraction approach of ancient Arabic documents based on image meshing technique, energy distribution of Cohen's class and connected component analysis techniques [20]. They defined a few rules depending on the orientations presented in their documents.…”

Section: Related Workmentioning

confidence: 99%

“…Le Bourgeois et al highlighted the need to design "intelligent" digitizers which can limit manual intervention and perform easy and high quality digitization of document images [2]. Therefore, with the support of the ANR (French National Research Agency 20 ) and the collaboration of many research laboratories, we are working on a project named DIGIDOC (Document Image diGitisation with Interactive DescriptiOn Capability) 21 . Specifically, the aim of the DIGIDOC project is to develop new ways of interacting with scanners and new tools for analyzing documents throughout the acquisition process, from scanning the document to knowledge representation and management of the digitized ancient document content.…”

Section: Introductionmentioning

confidence: 99%

A texture-based pixel labeling approach for historical books

Mehri

Gomez‐Krämer

Héroux

et al. 2015

Pattern Anal Applic

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Over the last few years, there has been tremendous growth in the automatic processing of digitized historical documents. In fact, finding reliable systems for the interpretation of ancient documents has been a topic of major interest for many libraries and the prime issue of research in the document analysis community. One important challenge is to refine well-known approaches based on strong a priori knowledge (e.g. the document image content, layout, typography, font size and type, scanning resolution, image size, etc.). Nevertheless, a texture analysis approach has consistently been chosen to segment a page layout when information is lacking on document structure and content. Thus, in this article a framework is proposed to investigate the use of texture as a tool for automatically determining homogeneous regions in a digitized historical book and segmenting its contents by extracting and analyzing texture features independently of the layout of the pages. The proposed framework is parameter-free and applicable to a large variety of ancient of books. It does not assume a priori information regarding document image content and structure. It consists of two phases: a texture-based feature extraction step and unsupervised clustering and labeling task based on the consensus clustering, hierarchical ascendant classification, and nearest neighbor search algorithms. The novelty of this work lies in the clustering of extracted texture descriptors to find automatically homogeneous regions, i.e. graphic and textual regions, by using the clustering approach on an entire book instead of processing each page individually. Our framework has been evaluated on a large variety of historical books and achieved promising results.

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“…Bottomup methods use simple rules, analyzing the geometric relationships between neighboring blocks such as the distance or the overlap. They have the merit to deal with noise problems and writing variation [7]. The common bottom-up method is based on the connected components [8], [28].…”

Section: Introductionmentioning

confidence: 99%

Arabic Handwritten Documents Segmentation into Text-Lines and Words using Deep Learning

Neche

Belaïd

Kacem-Echi

2019

2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)

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One of the most important steps in a handwriting recognition system is text-line and word segmentation. But, this step is made difficult by the differences in handwriting styles, problems of skewness, overlapping and touching of text and the fluctuations of text-lines. It is even more difficult for ancient and calligraphic writings, as in Arabic manuscripts, due to the cursive connection in Arabic text, the erroneous position of diacritic marks, the presence of ascending and descending letters, etc. In this work, we propose an effective segmentation of Arabic handwritten text into text-lines and words, using deep learning. For text-line segmentation, we used an RU-net which allows a pixel-wise classification to separate text-lines pixels from the background ones. For word segmentation, we resorted to the text-line transcription, as we have not got a ground truth at word level. A BLSTM-CTC (Bidirectional Long Short Term Memory followed by a Connectionist Temporal Classification) is then used to perform the mapping between the transcription and text-line image, avoiding the need of the input segmentation. A CNN (Convolutional Neural Network) precedes the BLST-CTC to extract the features and to feed the BLSTM with the essential of the text-line image. Tested on the standard KHATT Arabic database, the experimental results confirm a segmentation success rate of no less than 96.7% for text-lines and 80.1% for words.

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Segmentation of Ancient Arabic Documents

Cited by 11 publications

References 36 publications

Segmentation techniques for Arabic handwritten: a review

Segmentation techniques for Arabic handwritten: a review

A texture-based pixel labeling approach for historical books

Arabic Handwritten Documents Segmentation into Text-Lines and Words using Deep Learning

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