Update README.md

README.md

@@ -13,14 +13,14 @@ datasets:
 
 ## Model Description
 
-This is a *Convolutional Neural Network (CNN)* model trained to recognize *Hebrew letters* and a *stop symbols* in images. The model can identify individual letters from a provided image, outputting their respective class along with probabilities.
+This is a **Convolutional Neural Network (CNN)** model trained to recognize **Hebrew letters** and a **stop symbols** in images. The model can identify individual letters from a provided image, outputting their respective class along with probabilities.
 
 ## Model Details:
-* *Model Type*: Convolutional Neural Network (CNN)
-* *Framework*: TensorFlow 2.x / Keras
-* *Input Size*: 64x64 grayscale images of isolated letters.
-* *Output Classes*: 28 Hebrew letters + 1 stop symbol (.)
-* *Use Case*: Recognizing handwritten or printed Hebrew letters and punctuation in scanned images or photos of documents.
+* **Model Type**: Convolutional Neural Network (CNN)
+* **Framework**: TensorFlow 2.x / Keras
+* **Input Size**: 64x64 grayscale images of isolated letters.
+* **Output Classes**: 28 Hebrew letters + 1 stop symbol (.)
+* **Use Case**: Recognizing handwritten or printed Hebrew letters and punctuation in scanned images or photos of documents.
 
 ## Intended Use
 
@@ -60,45 +60,46 @@ If given an image with the Hebrew word "אברם" (Abram), the model can detect
 
 ## Limitations:
 
-* *Font Variations*: The model performs best on specific fonts (e.g., square Hebrew letters). Performance may degrade with highly stylized or cursive fonts.
-* *Noise Sensitivity*: Images with a lot of noise, artifacts, or low resolution may lead to incorrect predictions.
-* *Stop Symbol*: The stop symbol is particularly recognized by detecting three vertical dots. However, false positives can occur if letters with similar shapes are present.
+* **Font Variations**: The model performs best on specific fonts (e.g., square Hebrew letters). Performance may degrade with highly stylized or cursive fonts.
+* **Noise Sensitivity**: Images with a lot of noise, artifacts, or low resolution may lead to incorrect predictions.
+* **Stop Symbol**: The stop symbol is particularly recognized by detecting three vertical dots. However, false positives can occur if letters with similar shapes are present.
 
 ## Training Data:
 
 The model was trained on a dataset containing *Hebrew letters and stop symbols*. The training dataset includes:
 
-* *28 Hebrew letters*.
-* *1 stop symbol* representing three vertical dots (.).
+* **28 Hebrew letters**.
+* **1 stop symbol** representing three vertical dots (.).
 
 ## Training Procedure:
-* *Optimizer*: Adam
-* *Loss function*: Categorical Crossentropy
-* *Batch size*: 32
-* *Epochs*: 10
+* **Optimizer**: Adam
+* **Loss function**: Categorical Crossentropy
+* **Batch size**: 32
+* **Epochs**: 10
 
 Data augmentation was applied to reduce overfitting and increase the model's generalizability to unseen data. This includes random rotations, zooms, and horizontal flips.
 
 ## Model Performance
 
 # Metrics:
-* *Accuracy*: 95% on the validation dataset.
-* *Precision*: 94%
-* *Recall*: 93%
+* **Accuracy**: 95% on the validation dataset.
+* **Precision**: 94%
+* **Recall**: 93%
 * 
 Performance may vary depending on the quality of the input images, noise levels, and whether the letters are handwritten or printed.
 
 ## Known Issues:
-* *False Positives for Stop Symbols*: The model sometimes incorrectly identifies letters that resemble three vertical dots as stop symbols.
-* *Overfitting to Specific Fonts*: Performance can degrade on handwritten texts or cursive fonts not represented well in the training set.
+* **False Positives for Stop Symbols**: The model sometimes incorrectly identifies letters that resemble three vertical dots as stop symbols.
+* **Overfitting to Specific Fonts**: Performance can degrade on handwritten texts or cursive fonts not represented well in the training set.
 
 ## Ethical Considerations
 
-* *Bias*: The model was trained on a specific set of Hebrew fonts and may not perform equally well across all types of Hebrew texts, particularly historical or handwritten documents.
+* **Bias**: The model was trained on a specific set of Hebrew fonts and may not perform equally well across all types of Hebrew texts, particularly historical or handwritten documents.
 Fairness: The model may produce varying results depending on font style, quality of input images, and preprocessing applied.
-Future Work:
 
-* *Improving Generalization*: Future work will focus on improving the model's robustness to different fonts, handwriting styles, and noisy inputs.
+## Future Work:
+
+* **Improving Generalization**: Future work will focus on improving the model's robustness to different fonts, handwriting styles, and noisy inputs.
 Multilingual Expansion: Adding support for other Semitic scripts or expanding the model for multilingual OCR tasks.
 Citation: