Assistive Application for the Visually Impaired using Machine Learning and Image Processing
Bibliographic record
Abstract
The task of interpreting visual information poses a difficulty for artificial intelligence due to the intricate and diverse characteristics of visual data. Visual data can be disrupted and deficient, which complicates the process of machines attempting to precisely comprehend and decipher the meaning of an image. In this paper, a new method for image captioning for people who are blind is suggested. This method involves using a CNN-LSTM architecture, where a CNN is utilized to extract visual features from the image, and an LSTM generates a text-based description based on these features. A vast dataset of images and their corresponding captions are used to train the suggested model, and its effectiveness is assessed using the BLEU metric. Our model is validated using the benchmark dataset Flickr8K. The outcomes of the experiment demonstrate that the suggested technique has the capability to produce relevant and precise descriptions, which can help visually impaired people to access visual content. This method has the potential to fill the gap and provide a solution to the challenge of accessing visual media by the visually impaired.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".