Enhancing the Resolution of Historical Ottoman Texts Using Deep Learning-Based Super-Resolution Techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Ottoman Empire's extensive archives hold valuable insights into centuries of history, necessitating the preservation and transfer of this rich heritage to future generations.To facilitate access and analysis, numerous digitization efforts have been undertaken to transform these valuable resources into digital formats.The quality of digitized documents directly impacts the success of tasks such as text search, analysis, and character recognition.This study aims to enhance the resolution and overall image quality of Ottoman archive text images using four deep learning-based super-resolution (SR) algorithms: VDSR, SRCNN, DECUSR, and RED-Net.The performance of these algorithms was assessed using SSIM, PSNR, SCC, and VIF image quality measures (IQMs) and evaluated in terms of human visual system perception.All SR algorithms achieved promising IQM scores and a significant improvement in image quality.Experimental results demonstrate the potential of deep learning-based SR techniques in enhancing the resolution of historical Ottoman text images, paving the way for more accurate character recognition, text processing, and analysis of archival documents.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it