MSER-based text detection and communication algorithm for autonomous vehicles
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 and communication in the automotive context has attracted the attention of researchers only over the past few years. Detecting text in the automotive context, as opposed to the detection of text of printed pages, imposes additional challenges, such as the presence of obstacles, blurry frames, speedy vehicles, etc. In this paper, we present an in-vehicle real-time system able to localize texts and communicate them to the drivers. Our system begins by localizing regions of interest as a Maximally Stable Extremal Regions (MSERs). Afterwards, we apply a novel filtering stage which begins by dividing each ROI into 4 × 4 cells, counting the number of edges in each cell and comparing them to a well defined threshold. This is performed in order to filter out a considerable number of unwanted objects. The ROIs that contain text are fed into a recognition module based on the Optical Character Recognizer (OCR). Our proposed method achieves high f-scores when tested against several videos containing a numerous panels.
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 | 0.000 | 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 it