CV content recognition using YOLOv8 and Tesseract-OCR deep learning
Bibliographic record
Abstract
Abstract More effective sorting algorithms are required due to the growing number and variety of resumes in the employment market. Identifying suitable candidates for job openings from a large pool can be both repetitive and time-consuming, potentially leading to missed opportunities or biased selections due to human error. To address this challenge, this study presents a novel CV recognition system that integrates advanced technologies: You Only Look Once for detecting key sections within CVs, Tesseract-OCR for extracting text from these sections, and a series of post-processing steps to correct any text recognition errors. Additionally, the system includes an automated data organization component that stores CV information in a database, facilitating data analytics and search operations. The system was evaluated using a public dataset of 1300 resumes in JPEG, PNG, and JPG formats, sourced from various origins and reflecting diverse formats, languages, and quality levels. Preprocessing was conducted to ensure data consistency and quality. The hyperparameters of the models were optimized using a genetic algorithm. The proposed system significantly enhances efficiency and accuracy in resume sorting, allowing HR teams to concentrate on strategic tasks and streamline the hiring process. Experimental results demonstrate the system’s effectiveness, achieving a mean average precision of 92.1%, a precision rate of 92.2%, and a recall rate of 86.0%.
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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 | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".