Research on automatic segmentation and recognition of original topographic single characters based on intelligent recognition of oracle bones
Bibliographic record
Abstract
The study of oracle bones is of great significance to the understanding of the development of Chinese and foreign civilizations, with the development of artificial intelligence computing, the text recognition of oracle bones has a more efficient method, and the use of machine vision related technology to achieve the text segmentation and text recognition of oracle bone topography can effectively improve the efficiency of the study of oracle bones. In this paper, the establishment of a series of models from the pre-processing of oracle bone topographies, the segmentation of oracle bone text to oracle bone text recognition is investigated. In this paper, we first preprocess the image of oracle bone topographies to eliminate the elements other than topographies, such as numbers and letters in the numbering, etc., and then use commonly used machine vision techniques to filter the image to reduce the impact of interference factors on oracle bone recognition, use image enhancement and image binarization techniques to make the text elements in the image more prominent, and finally use edge extraction techniques to extract the edge information of the text in the topographies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".