Indoor Sign Recognition System for Visually Impaired People
Bibliographic record
Abstract
Vision impairment can result in a loss of independence due to the inability to fully understand the surrounding environment. Indoor signage is a crucial factor in enabling visually impaired people to navigate unfamiliar environments. This paper proposes a system that automatically recognises a variety of indoor signs, including accessibility, elevator, exit, female toilet, male toilet, no smoking, restaurant, and Wi-Fi. Developing deep-learning algorithms that can accurately recognise similar signs or significant variations is challenging. To address this, the proposed method utilises a dataset of 1141 RGB images to train and test the system. Our approach employs a pre-trained AlexNet architecture with transfer learning and a customised sequential CNN architecture. Experimental results indicate an accuracy rate of 63% for the customised sequential CNN and 80% for AlexNet with transfer learning. The proposed system has the potential to enhance the independence and freedom of visually impaired individuals significantly.
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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 | 0.000 | 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 | 0.001 |
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".