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README.md

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-license: mit
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+# MNIST Pattern Recognition with Convolutional Neural Network (CNN)
+
+This project implements a Convolutional Neural Network (CNN) for recognizing handwritten digits from the MNIST dataset. The model is built using TensorFlow and Keras, and it supports both single-GPU and multi-GPU training. The project includes training, testing, and a user-friendly GUI for inference.
+
+## Features
+- **Customizable CNN Architecture**: Includes convolutional, pooling, normalization, and dense layers.
+- **Multi-GPU Support**: Leverages TensorFlow's `MirroredStrategy` for distributed training.
+- **Training Visualization**: Generates plots for training/validation accuracy and loss.
+- **Evaluation Metrics**: Outputs confusion matrix, classification report, and precision/recall/F1 scores.
+- **Interactive GUI**: Built with Streamlit for real-time image recognition.
+- **Docker Support**: Easily deployable using Docker.
+
+## Model Architecture
+![image](model.png) <br>
+The CNN model consists of:
+1. Two convolutional layers with ReLU activation and max-pooling.
+2. Layer normalization for improved convergence.
+3. Fully connected dense layers with dropout for regularization.
+4. Softmax output layer for classification into 10 digit classes.
+
+## Training
+The model is trained on the MNIST dataset, which contains 60,000 training images and 10,000 test images of handwritten digits (28x28 grayscale). The training process includes:
+- Data normalization to scale pixel values to the range [0, 1].
+- Categorical cross-entropy loss and accuracy as the evaluation metric.
+- Model checkpointing to save the best-performing model based on validation accuracy.
+
+## Final result
+**Training history**
+![image](training_history.png) <br>
+
+**Confusion matrix**
+![image](confusion_matrix.png) <br>
+
+**Classification report**
+![image](classification_report_image.png) <br>
+
+**Test result**
+![image](test_result.png) <br>
+
+Full code at [Github](https://github.com/longdnk/Pattern-Recognition/tree/main/MNIST)