DoubleYolo: Efficient Scene Text Detection Using Double Edge Method and YOLOv8n
Bibliographic record
Abstract
Day by day environmental and architectural changes influenced the society and world, scene texts are also changing with various styles and dimensions.There is need to detect and understanding of text in scene images like name plates, bill boards and bus routes to assist tourists and automated environments.Scene text detection poses various challenges like complex background, multilingual, multi-orientation, occlusion and poor lighting effects.Many methods have developed using machine learning and deep learning models but not achieved significant impact due to either heaviness of models and involve much training and testing of large number of images.Hence the proposed algorithm implemented the double edge method with YOLOv8n to detect scene text in images.In real world scenario, the text components exhibit double line structures with cyclic edges in nature.Using this property double edge method retains the prominent text components at primary stage.Further by employing YOLOv8n which refines the fine-grained textual components from scene images.The proposed algorithm is simple approach and yields better efficacy even with the smaller number of trained samples.The experimentation conducted on benchmark datasets like CTW1500, MSRA TD500, Total Text, MRRC, and MLe2e and serves handy for scene text detection/recognition tasks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".