Enhanced Text Extraction: Combining Bacteria Foraging Optimization Algorithm-Optimized Scale-Invariant Feature Transform with Machine Learning for Robust Performance
Bibliographic record
Abstract
This research presents a novel hybrid method for robust text retrieval from images captured under varying illumination and background conditions-challenges where conventional deep learning models often struggle.The proposed approach combines Scale-Invariant Feature Transform (SIFT) for keypoint detection with the Bacteria Foraging Optimization Algorithm (BFOA) to optimize feature selection and reduce computational complexity.A Random Forest (RF) classifier is then employed for final classification, offering improved generalization under diverse visual environments.Unlike existing deep learning approaches, this BFOA-optimized SIFT+RF pipeline achieves higher accuracy with lower processing overhead.On benchmark datasets, the proposed model achieves a retrieval accuracy of 92.4%, outperforming baseline convolutional neural network (CNN) models by 7.1%, while maintaining consistent performance under variable lighting conditions.These results highlight the method's novelty and effectiveness, making it well-suited for applications such as document digitization, scene understanding, and image-based text retrieval.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.002 |
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".