Penerapan Metode Thresholding Pada Proses Transformasi Citra Digital
Bibliographic record
Abstract
Book is a window to the world. By reading books, we can get a variety of knowledge that we do not know. The meaning of books as windows to the world can be doubted today. Given the internet provides a lot of information with a much more attractive appearance. Both books and the internet provide a wealth of information and knowledge. But in fact books are still the mainstay of accurate sources of knowledge. A lot of information on the internet is in fact not supported with accuracy. Even in the world of higher education, books are the main reference source in scientific writing. However, old books that may have started to become dull and the writing can no longer be read resulted in the reader of the book not being able to know the knowledge contained in the old book. Based on observations, it is necessary to build a computerized system so that the writings in the book can be read again. One method that can be used is the Thresholding method. The system is designed with MATLAB programming application. In this process, it is necessary to scan or take photos/pictures from cellphone cameras from old book manuscripts to be processed on the system using the Thresholding method. After carrying out the testing process on several images of old books, better results were obtained so that the writing on the manuscript could be seen more clearly with different threshold values for each image entered into the system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.019 |
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".