TEDLESS – Text detection using least-square SVM from natural scene
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Text detection from the natural scene is considered to be a challenging problem due to the complex background, varied light intensity at different locations, a large variety of colors, diverse font style and size. This paper focusses on detecting candidate text objects from the scene. The image is initially preprocessed to remove the noise and enhance the contrast. Then the various objects of the scene are marked and extracted forming a pool of objects. A set of candidate text objects are extracted from this pool of objects and given as output. In order to locate text candidates among these objects, we use Least-Square Support Vector Machine Technique, which trains the model using Char 74K character dataset and CIFAR 10 non-text image dataset. Finally, the trained model was applied to perform a binary classification of text and non-text objects. The results were evaluated over ICDAR 2015 scene images, MSRA500 and SVT datasets and also have been compared to other approaches acquiring encouraging results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.016 |
| Open science | 0.001 | 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 it