Handwritten Character Recognition using Convolutional Neural Networks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0bb0fc57",
   "metadata": {},
   "source": [
    "# Handwritten Character Recognition using Convolutional Neural Networks\n",
    "\n",
    "## Description:\n",
    "This project aims to demonstrate the use of Convolutional Neural Networks (CNNs) for recognising handwritten characters from the MNIST dataset. The MNIST dataset consists of 28x28 pixel grayscale images of handwritten digits (0-9). The goal is to train a CNN model to accurately classify these images into their respective digit classes.\n",
    "\n",
    "## Tools and Technologies:\n",
    "- Python\n",
    "- TensorFlow\n",
    "- Keras\n",
    "- Jupyter Notebook\n",
    "\n",
    "## Project Steps:\n",
    "1. **Data Preparation:** Load the MNIST dataset and preprocess the images by normalizing pixel values.\n",
    "2. **Model Building:** Construct a CNN model architecture using TensorFlow and Keras.\n",
    "3. **Model Training:** Train the CNN model on the training dataset.\n",
    "4. **Model Evaluation:** Evaluate the trained model's performance on the test dataset.\n",
    "5. **Prediction:** Make predictions on unseen test images using the trained model.\n",
    "6. **Visualization:** Visualize the model's predictions along with the corresponding true labels.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "941f0d1d",
   "metadata": {},
   "source": [
    "## Step 1: Install and Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ea73ff8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "WARNING: There was an error checking the latest version of pip.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda Feenah\\lib\\site-packages\\scipy\\__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.25.0 is required for this version of SciPy (detected version 1.26.4\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "# Install necessary libraries\n",
    "!pip install tensorflow matplotlib\n",
    "\n",
    "# Import required libraries\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dfd4c90",
   "metadata": {},
   "source": [
    "## Step 2: Load and Preprocess Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "30bc5f32",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the MNIST dataset\n",
    "mnist = tf.keras.datasets.mnist\n",
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\n",
    "\n",
    "# Normalize pixel values to range between 0 and 1\n",
    "train_images, test_images = train_images / 255.0, test_images / 255.0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e8795cc",
   "metadata": {},
   "source": [
    "## Step 3: Build CNN Model Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c5b58f08",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda Feenah\\lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:99: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(\n"
     ]
    }
   ],
   "source": [
    "# Build the CNN model architecture\n",
    "model = models.Sequential([\n",
    "    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n",
    "    layers.MaxPooling2D((2, 2)),\n",
    "    layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    layers.MaxPooling2D((2, 2)),\n",
    "    layers.Flatten(),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc3ad423",
   "metadata": {},
   "source": [
    "## Step 4: Compile the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "00019034",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76a2b620",
   "metadata": {},
   "source": [
    "## Step 5: Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c2b1f2d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 12ms/step - accuracy: 0.9067 - loss: 0.3079 - val_accuracy: 0.9826 - val_loss: 0.0491\n",
      "Epoch 2/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 12ms/step - accuracy: 0.9863 - loss: 0.0460 - val_accuracy: 0.9886 - val_loss: 0.0351\n",
      "Epoch 3/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 11ms/step - accuracy: 0.9914 - loss: 0.0286 - val_accuracy: 0.9895 - val_loss: 0.0320\n",
      "Epoch 4/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 12ms/step - accuracy: 0.9935 - loss: 0.0202 - val_accuracy: 0.9900 - val_loss: 0.0314\n",
      "Epoch 5/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 12ms/step - accuracy: 0.9947 - loss: 0.0152 - val_accuracy: 0.9909 - val_loss: 0.0265\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "history = model.fit(train_images[..., tf.newaxis], train_labels, epochs=5, batch_size=32, validation_data=(test_images[..., tf.newaxis], test_labels))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3256da7",
   "metadata": {},
   "source": [
    "## Step 6: Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "acfb2bcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9877 - loss: 0.0341\n",
      "Test accuracy: 0.9908999800682068\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the test dataset\n",
    "test_loss, test_acc = model.evaluate(test_images[..., tf.newaxis], test_labels)\n",
    "print(f'Test accuracy: {test_acc}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a9b5ccd",
   "metadata": {},
   "source": [
    "## Step 7: Make Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d3f1e82a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on test images\n",
    "predictions = model.predict(test_images[..., tf.newaxis])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a367036",
   "metadata": {},
   "source": [
    "## Step 8: Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2e6534d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the model's predictions\n",
    "plt.figure(figsize=(10, 10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5, 5, i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(test_images[i], cmap=plt.cm.binary)\n",
    "    predicted_label = tf.argmax(predictions[i]).numpy()\n",
    "    true_label = test_labels[i]\n",
    "    if predicted_label == true_label:\n",
    "        color = 'green'\n",
    "    else:\n",
    "        color = 'red'\n",
    "    plt.xlabel(f'Predicted: {predicted_label}, True: {true_label}', color=color)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7f31aef",
   "metadata": {},
   "source": [
    "## Conclusion:\n",
    "By leveraging CNNs, we can achieve accurate handwritten character recognition, which can have various applications such as digitizing historical documents, automated form processing, and handwriting recognition in digital devices.\n",
    "\n",
    "## Next Steps:\n",
    "- Experiment with different CNN architectures and hyperparameters to improve model performance.\n",
    "- Explore other handwritten character datasets for broader applications.\n",
    "- Deploy the trained model in real-world applications for character recognition tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2d7534e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}