Bibliographic record
Abstract
This paper introduces a AI-based OCR post-processing technique, implemented as an Intelligent OCR Editor (IOCRED), which could enable the automation of OCR post-processing procedure and, therefore, could result in the increase of throughput, the decreases of error rate and the reduction of cost per page of an OCR system. An IOCRED system consists of a number of commercially available but different OCR systems performing text conversion on the same page simultaneously; a comparator detecting errors in the conversion results of OCRs; and, an intelligent error correction system using AI techniques such as expert systems, neural networks and fuzzy logic, to correct the detected errors. The IOCRED system is based on the premise that different OCR algorithms have distinct error characteristics. Such distinctions can be utilized by a cognitive device to detect and correct the errors in the conversion results of OCRs. To prove the concept, a statistical analysis of the error characteristics of OCR systems was conducted. Three popular commercial OCR systems were chosen for the study. The results showed that these OCR systems have distinct error characteristics and it is possible to achieve a high accuracy OCR conversion utilizing these differences. A simulation system used to examine the performance of the proposed IOCRED system was developed. The results of the simulations showed that utilizing the IOCRED system to achieve a high throughput, low error rate and low cost OCR conversion can be expected.>
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.146 | 0.108 |
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".