Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsA convolutional neural network (CNN) achieved 97.80% accuracy and 97.80% macro F1 on a held-out test set of 7,172 static ASL letter images in the reported project. Those figures describe recognition of isolated letter images in the Sign Language MNIST dataset—not full American Sign Language translation or proven performance with new signers and camera conditions.
What the project recognizes
The project compares four classifiers on images of static handshapes representing ASL letters. Its dataset description lists 24 letter classes; J and Z are excluded because those letters involve movement. A model trained on these still images can classify the represented letters, but it does not interpret movement, sequences, grammar, or meaning in full sign-language communication.
The project describes Sign Language MNIST as 28 × 28-pixel grayscale images, with 27,455 images for training and 7,172 held out for testing. The original training portion was further split into 23,336 images for training and 4,119 for validation. Pixel values were scaled to the 0-to-1 range by dividing by 255. The reported results are from the project article by Levina; its publication year is not established.
How the four classifiers were compared
The comparison combines different classifier families with different input representations. That matters: the CNN receives the image itself, while the other three classifiers receive a compact vector of averaged pixel values.
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| Classifier | Role in comparison | Input representation |
|---|---|---|
| Logistic Regression | Baseline | 49 features formed by averaging pixels in 4 × 4 blocks |
| Random Forest | Bagging ensemble | 49 features formed by averaging pixels in 4 × 4 blocks |
| Histogram Gradient Boosting | Boosting ensemble | 49 features formed by averaging pixels in 4 × 4 blocks |
| CNN | Convolutional neural network | Original 28 × 28 grayscale image |
For the non-CNN models, each image is divided into 4 × 4 pixel blocks, and the pixels in each block are averaged. The resulting 7 × 7 grid yields 49 features. The models were evaluated with three-fold stratified cross-validation on the 23,336-image training portion; the project created a fresh CNN for each fold. It also tuned Random Forest settings across tree count, maximum depth, and minimum samples per leaf.
What the reported scores mean
The headline result is the CNN’s performance on the separate 7,172-image test set. The Random Forest figure below is a best validation score, not a test result. These metrics come from the project’s particular split and pipeline and are not reported as independently reproduced results.
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| Model and result | Split and metric | How to interpret it |
|---|---|---|
| CNN: 97.80% | Held-out test accuracy on 7,172 images | Share of test images classified correctly |
| CNN: 97.80% | Held-out test macro F1 on 7,172 images | Unweighted average of per-class F1 scores |
| Tuned Random Forest: 98.70% | Best validation macro F1 | Validation score; not the Random Forest’s test score |
Because accuracy and macro F1 are different measures, the matching 97.80% CNN values should not be treated as interchangeable definitions of performance. Accuracy counts overall correct classifications; macro F1 averages the F1 score for each class equally. The article reports the CNN as strongest during cross-validation and validation as well as reporting its held-out test results. Among the ensemble methods, Random Forest outperformed Histogram Gradient Boosting, and both outperformed Logistic Regression.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the results do—and do not—establish
Comparative performance is not a controlled input comparison
The scores compare complete pipelines, not classifier algorithms with all other factors held constant. The CNN learns from full images; the other classifiers work from 49 block averages. The CNN result therefore cannot be attributed solely to its classifier family, nor do these results establish which classifier would win if every model used the same representation.
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Test-set success is not evidence of field performance
The evaluation uses one image dataset and does not establish how the model performs with different people signing, lighting, backgrounds, or camera angles. Those are unanswered generalization questions, not conditions the reported test demonstrates.
Some letters were harder for the CNN
The CNN classification report gives recall of about 0.88 for T, 0.91 for S, and 0.92 for I, lower than for the other reported classes. Recall reflects the share of examples of a particular class that the model identified correctly. The article describes a confusion matrix, but does not provide enough detail to responsibly state further error counts.
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How to read this project’s takeaway
- It is a study of static letter-image classification, not an ASL conversation or translation system.
- The CNN’s reported 97.80% accuracy and 97.80% macro F1 apply to the project’s 7,172-image held-out test set.
- The Random Forest’s 98.70% is its best validation macro F1, so it should not be ranked against the CNN’s test figures as though both scores came from the same split.
- The model comparison changes both the classifier and the input representation, and the evaluation does not settle performance on new signers or real-world camera conditions.
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