---
title: "Off-Line Arabic Handwritten Words — AbdAllah 2021"
description: "The paper addresses segmentation of offline handwritten Arabic words into sub-words, also called pieces of Arabic words (PAWs)."
authors: ["Nisreen AbdAllah", "Serestina Viriri"]
year: 2021
venue: "arXiv:2101.02797 · Signal & Image Processing: An International Journal (SIPIJ) Vol.11, No.6, December 2020"
source: "https://arxiv.org/abs/2101.02797"
doi: "10.5121/sipij.2020.11602"
arxiv: "2101.02797"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/abdallah-2021-off-line-arabic-handwritten-words/"
---
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# Off-Line Arabic Handwritten Words Segmentation using Morphological Operators

**Authors:** Nisreen AbdAllah, Serestina Viriri · **Year:** 2021 · **Venue:** arXiv:2101.02797 · Signal & Image Processing: An International Journal (SIPIJ) Vol.11, No.6, December 2020 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://doi.org/10.5121/sipij.2020.11602) · [PDF](https://arxiv.org/pdf/2101.02797) · [DOI 10.5121/sipij.2020.11602](https://doi.org/10.5121/sipij.2020.11602) · [arXiv 2101.02797](https://arxiv.org/abs/2101.02797) · [OpenAlex](https://openalex.org/W3119795144)

**Citations:** 2 citations (OpenAlex, 2026-10-14)

## Summary
The paper addresses segmentation of offline handwritten Arabic words into sub-words, also called pieces of Arabic words (PAWs). Arabic is cursive, and six letters never join to the following letter, so a word naturally breaks into several connected pieces. Segmentation becomes unreliable when strokes are accidentally broken by pen lifts, faded ink, scanning, or binarization, and the authors identify such gaps as a major source of errors.

They propose a three-stage pipeline:
- **Pre-processing:** binarization, a combination of three morphological operators to connect gaps (the "CGs" step), and skeletonization.
- **Segmentation:** small diacritics such as dots are removed, then each remaining connected component is enclosed in a bounding box as a sub-word.
- **Evaluation:** results are scored automatically against database ground truth.

The authors chose IESK-ArDB because, unlike IFN/ENIT, it provides sub-word positions in its ground truth. On 1,131 randomly selected images, the method reached 88% agreement with the ground truth after gap connection, which the authors report as higher than comparable earlier work.

## Key points
- Arabic has 28 letters, each with 2–5 positional shapes. Fifteen letters carry dots, and six connect only on one side, which produces the sub-word (PAW) structure.
- The evaluation used 1,131 randomly chosen images from IESK-ArDB, and segmentation accuracy reached 88% against the database ground truth.
- The method uses morphological gap closing in pre-processing, then diacritic removal and connected-component bounding boxes for segmentation.
- Comparison points from related work:
  - AlKhateeb et al. reached 85% on 200 IFN/ENIT images.
  - Ghaleb et al.'s pushing technique reached 72%.
  - A bounding-box distance method tested on 450 IESK-ArDB images reported no overall accuracy.
- IFN/ENIT lacks character- or sub-word-level positions, so segmentation scored on it needs manually added annotation files.

## Interesting ideas
- Repair accidental fragmentation before splitting into units, so that segmentation boundaries reflect real structure rather than noise. — For Mova (tokens): Before tokenization, a normalization pass could rejoin words split by line-break hyphens or broken extraction, so the tagger and lemmatizer see whole words.
- Remove small secondary marks (dots, diacritics) before finding the main units, so tiny pieces are not mistaken for units of their own. — For Mova (tokens): The tokenizer could set aside punctuation and similar attachments when finding word bodies, then reattach them deterministically, which keeps each step explainable.
- Pick an evaluation dataset whose ground truth labels the exact units being produced, so scoring can be fully automatic. — For Mova (learn): Scoring should prefer treebanks and test sets with gold annotation at the same granularity as Mova's output, instead of patching in hand-made alignment files.
- The number of expected pieces follows from a known rule (which letters do not join), giving a structural expectation to check output against. — For Mova (learn): Rule-derived expectations, such as the clause count implied by verbs and conjunctions, could act as cheap consistency checks that flag suspicious silver parses for repair.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Morphology](https://md.research.elamur.ai/free/en/topics/morphology/) · [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{abdallah2021off,
  title = {{Off-Line Arabic Handwritten Words Segmentation using Morphological Operators}},
  author = {AbdAllah, Nisreen and Viriri, Serestina},
  year = {2021},
  doi = {10.5121/sipij.2020.11602},
  eprint = {2101.02797},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.5121/sipij.2020.11602}
}
```

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