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New Results for the Text Recognition of Arabic Maghrib{\=i} Manuscripts -- Managing an Under-resourced Script
Computer Vision and Pattern Recognition · Submitted 29 Nov 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The comparison between several state-of-the-art HTR demonstrates the relevance of a word-based neural approach specialized for Arabic, capable to achieve an error rate below 5% with only 10 pages manually transcribed.
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
HTR models development has become a conventional step for digital humanities projects. The performance of these models, often quite high, relies on manual transcription and numerous handwritten documents. Although the method has proven successful for Latin scripts, a similar amount of data is not yet achievable for scripts considered poorly-endowed, like Arabic scripts. In that respect, we are introducing and assessing a new modus operandi for HTR models development and fine-tuning dedicated to the Arabic Maghrib{\=i} scripts. The comparison between several state-of-the-art HTR demonstrates the relevance of a word-based neural approach specialized for Arabic, capable to achieve an error rate below 5% with only 10 pages manually transcribed. These results open new perspectives for Arabic scripts processing and more generally for poorly-endowed languages processing. This research is part of the development of RASAM dataset in partnership with the GIS MOMM and the BULAC.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | HTR models development has become a conventional step for digital humanities projects. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | The performance of these models, often quite high, relies on manual transcription and numerous handwritten documents. | background 0.98 | 0.09 | 0.03 | asserted 0.97 |
| s2 | Although the method has proven successful for Latin scripts, a similar amount of data is not yet achievable for scripts considered poorly-endowed, like Arabic scripts. | gap 0.84 | 0.07 | 0.03 | asserted 0.96 |
| s3 | In that respect, we are introducing and assessing a new modus operandi for HTR models development and fine-tuning dedicated to the Arabic Maghrib{\=i} scripts. | contribution 1.00 | 0.83 | 0.08 | asserted 0.99 |
| s4 | The comparison between several state-of-the-art HTR demonstrates the relevance of a word-based neural approach specialized for Arabic, capable to achieve an error rate below 5% with only 10 pages manually transcribed. | result 1.00 | 0.89 | 0.82 | asserted 1.00 |
| s5 | These results open new perspectives for Arabic scripts processing and more generally for poorly-endowed languages processing. | interpretation 0.98 | 0.43 | 0.39 | asserted 0.61 |
| s6 | This research is part of the development of RASAM dataset in partnership with the GIS MOMM and the BULAC. | background 0.52 | 0.48 | 0.03 | asserted 0.99 |
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The paper
Original abstract
HTR models development has become a conventional step for digital humanities projects. The performance of these models, often quite high, relies on manual transcription and numerous handwritten documents. Although the method has proven successful for Latin scripts, a similar amount of data is not yet achievable for scripts considered poorly-endowed, like Arabic scripts. In that respect, we are introducing and assessing a new modus operandi for HTR models development and fine-tuning dedicated to the Arabic Maghrib{\=i} scripts. The comparison between several state-of-the-art HTR demonstrates the relevance of a word-based neural approach specialized for Arabic, capable to achieve an error rate below 5% with only 10 pages manually transcribed. These results open new perspectives for Arabic scripts processing and more generally for poorly-endowed languages processing. This research is part of the development of RASAM dataset in partnership with the GIS MOMM and the BULAC.
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