Advancing Ancient Arabic Manuscript Restoration with Optimized Deep Learning and Image Enhancement Techniques
Bibliographic record
Abstract
The restoration of ancient Arabic manuscripts is a challenging task because of the noise and degradations present in restored historical documents.This paper presents an effective pipeline for manuscript restoration based on the Data Augmentation concepts and the GA for improving DL models.The Genetic Algorithm was chosen because it helps to optimize deep learning frameworks in an effort to improve the model in question with respect to restoring the manuscripts in question.Also, principles like CLSR and Wiener Filter help in noise reduction and enhancement of the images during image restoration.The findings suggest significant improvements in terms of accuracy, elimination of noise, image clarity and resolution, as well as the general readability of the restored manuscripts with accuracy rates of up to 97%.70% for NASNet-A, 98. 40% for EfficientNet-B7 and 99.13% for AmoebaNet-A.Apart from outperforming current procedures, these results support the protection and academic study of ancient Arabic manuscripts.Even as we continue to emphasize on the importance of these manuscripts within our culture and the on-going efforts to preserve them, it is also important to highlight the other areas in which our methods can apply.All these techniques have the potentiality of solving restoration problems in other types of manuscripts and can be adopted for other image restoration problems.This research contributes to the collection of essential information and tools for scholars and other interested individuals involved in the preservation of these important cultural artifacts and attempts to expand the application of these methods to address a wider range of restoration issues based on the results of this research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".