Document Recognition in Education Sector Using Machine Learning Algorithms
Bibliographic record
Abstract
Machine learning (ML) is a widespread technique in classification tasks, fraud detection, time series analysis, and many such challenging problems.One such problem in the education sector is recognizing the different types of educational credentials to verify the genuineness of such credentials.We have not found any research work to identify educational documents using ML.The objective of this work is to recognize the educational credentials across multiple categories to avoid manual processes.This novel study recognizes credentials from large datasets into five categories: Choice-Based Credit System (CBCS), Computerized, Handwritten, Non-CBCS, and Degree Certificate.The two statistical feature extraction methods, namely Gray Level Cooccurrence Matrix (GLCM), and Gray Level Histogram Analysis (GLHA) were used to extract the features from these credentials, and we stored them in the database.These features were trained using six supervised classifier algorithms including Naï ve Bayes (NB), Multinomial Logistic Regression (MLR), Decision Tree (DT), K-Nearest Neighbours (KNN), Support Vector Machine (SVM), and Random Forest (RF) to recognize the credentials.We analyzed the performances of these algorithms as follows: (i) the impact of hyper-parameters on each algorithm, (ii) the performance of two feature extraction methods, and (iii) the recommendation of classifiers based on accuracy.SVM yielded good results using the GLCM feature extraction method among the above-mentioned algorithms, attaining a high accuracy of 98.4%.This approach can also recognize documents like employment agreements, contracts agreements, financial statements, etc., in business, and industry sectors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".