Geometric Deep Learning for Enhancing Irregular Scene Text Detection
Bibliographic record
Abstract
Text detection in natural scene images presents significant challenges, particularly in detecting irregular shapes.As a result of the limited receptive field of CNNs, existing methods have difficulty capturing long-range relationships between distant component regions.This study introduces an innovative method for identifying irregular text in images of natural scenes.The approach utilizes a U-net architecture combined with connected component analysis, resulting in improved accuracy in detecting text components and reducing the identification of non-character text components.Additionally, our strategy incorporates the use of graph convolution networks (GCN) to deduce adjacency relations among text components.The integration of GCNs introduces a sophisticated mechanism for inferring adjacency relations, contributing significantly to the advancement of text detection in natural scene images.Our method's efficacy is showcased through experimental assessments on three publicly available datasets: "ICDAR2013," "CTW-1500," and "MSRA-TD500."
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".