Image Processing in Improving the Scan Results of Identity Cards and Family Cards with Noise with the Median Filtering Method
Bibliographic record
Abstract
In the digital era, identity documents such as Identity Cards and Family Cards play a crucial role in administration and public services. The process of scanning these documents often results in images with noise that can interfere with data accuracy and identification. This research aims to improve the quality of scanned images by using the median filtering method to reduce salt and pepper noise. Median Filtering was chosen for its ability to preserve edges and details in images, which is crucial for identity documents such as Identity Cards and Family Cards. In this study, the system developed to enhance scanned images was implemented and tested. Evaluation results show that the Median Filtering method is effective in removing noise without damaging the edges and fine details of the images. The evaluation indicates that this system is effective in producing images that meet the quality standards required for official administration and identification. This research is expected to contribute to the development of digital image enhancement technology for identity documents, thus improving accuracy and reliability in public services.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".