Hindi_Character_Recognition.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Hindi Character Recognition Using Convolutional Neural Network"
      ],
      "metadata": {
        "id": "43GbLU5lRHmM"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_jGCusgCQwMi"
      },
      "source": [
        "**<h3>About Dataset**\n",
        "\n",
        "\n",
        "Context\n",
        "\n",
        "Devanagari is an Indic script used in India and Nepal, and our dataset contains 36 characters and 10 digits. It is differentiable from many other written languages by the lack of capitalization and the horizontal bar aligned along the top of the script. Below are some examples of input used for this project. These are handwritten symbols from the training dataset used to train our model. Note that each of the following is different characters, although some of them appear quite similar - this is the problem that our model will attempt to resolve. Since these are handwritten characters, some amount of error can be attributed to sloppy or perhaps illegible writing - it might not be possible for even a human subject-matter to expect to achieve 100% accuracy.\n",
        "\n",
        "**Content**\n",
        "\n",
        "Our training dataset contains 2000 examples of each character, for a total of 92,000 images. Each image consists of 32x32 pixels and 3 color channels. The test set consists of 13800 total images (300 for each character) and the training set consists of 78200 images (1700 per character). This accounts for an 85/15 split.\n",
        "\n",
        "\n",
        "\n",
        "**Inspiration**\n",
        "\n",
        "Character recognition (for any language) is important to transcribe the written text into digital representations. For many difficult-to-read handwritten instances of lettering, human intervention is required via methods such as Captcha, Gamification, or manual annotation. This process is often labor and cost-expensive. However, for many character sets, deep learning models can accurately transcribe handwriting to digital encoding\n",
        "\n",
        "Dataset Source : https://www.kaggle.com/datasets/suvooo/hindi-character-recognition/code"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGyWxrlvmKRg"
      },
      "source": [
        "**<h3>Problem Statement :**\n",
        "\n",
        "\n",
        "To build a deep learning model that can accurately recognize Hindi characters from input images using CNN.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h3>Performance Metrics :**\n",
        "\n",
        "1.    Accuracy\n",
        "2. Confusion Metrics"
      ],
      "metadata": {
        "id": "dU92rkmbRd70"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<h3>References :**\n",
        "\n",
        " https://medium.com/@Suraj_Yadav/hindi-character-recognition-with-machine-learning-and-deep-learning-56cc4005c433"
      ],
      "metadata": {
        "id": "I4VxMlHITFp5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OxIGNv0LSDKJ",
        "outputId": "c6e173e1-ee5f-4fbb-8da1-871307c40d08"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.9/dist-packages (1.5.13)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.9/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.9/dist-packages (from kaggle) (1.26.15)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.9/dist-packages (from kaggle) (2022.12.7)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.9/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from kaggle) (4.65.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.9/dist-packages (from kaggle) (8.0.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from kaggle) (2.27.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.9/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->kaggle) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->kaggle) (3.4)\n"
          ]
        }
      ],
      "source": [
        "# Installing useful libraries\n",
        "! pip install kaggle\n",
        "import os\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.preprocessing import image_dataset_from_directory\n",
        "from sklearn.metrics import accuracy_score,classification_report\n",
        "from keras import optimizers\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Convolution2D,MaxPooling2D,Activation, Dropout, Flatten, Dense, BatchNormalization\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "from tqdm.notebook import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "XFlOnZ1XAzdX"
      },
      "outputs": [],
      "source": [
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "yD67UmUmSStA"
      },
      "outputs": [],
      "source": [
        "! chmod 600 ~/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S3DBqRL3STXw",
        "outputId": "91259806-fcd2-4456-c773-2d9a475181b9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading hindi-character-recognition.zip to /content\n",
            " 96% 73.0M/76.1M [00:00<00:00, 264MB/s]\n",
            "100% 76.1M/76.1M [00:00<00:00, 252MB/s]\n"
          ]
        }
      ],
      "source": [
        "# Downloading Kaggle Dataset \n",
        "!kaggle datasets download --force suvooo/hindi-character-recognition"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u7r1VOQSm4U3",
        "outputId": "757899c4-f7bc-44fc-8c61-76421e17570f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "# Unzipping Zipped file\n",
        "np.random.seed(10)\n",
        "!unzip '/content/hindi-character-recognition.zip' -d '/content/Hindi Character Recognition'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "pgBWl31hSfwB"
      },
      "outputs": [],
      "source": [
        "# Train and Test Directory\n",
        "train_dir = '/content/Hindi Character Recognition/DevanagariHandwrittenCharacterDataset/Train'\n",
        "test_dir = '/content/Hindi Character Recognition/DevanagariHandwrittenCharacterDataset/Test'\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lFlS0pp7l5f6",
        "outputId": "b54b388c-befa-4e44-a4cd-cf6116f57b57"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "46\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['character_26_yaw',\n",
              " 'character_23_ba',\n",
              " 'character_28_la',\n",
              " 'character_19_dha',\n",
              " 'character_27_ra',\n",
              " 'digit_9',\n",
              " 'character_31_petchiryakha',\n",
              " 'digit_0',\n",
              " 'character_24_bha',\n",
              " 'character_11_taamatar',\n",
              " 'character_3_ga',\n",
              " 'character_20_na',\n",
              " 'character_22_pha',\n",
              " 'character_2_kha',\n",
              " 'character_15_adna',\n",
              " 'digit_2',\n",
              " 'character_14_dhaa',\n",
              " 'digit_5',\n",
              " 'character_36_gya',\n",
              " 'character_9_jha',\n",
              " 'character_5_kna',\n",
              " 'digit_3',\n",
              " 'digit_6',\n",
              " 'character_33_ha',\n",
              " 'character_21_pa',\n",
              " 'digit_7',\n",
              " 'character_8_ja',\n",
              " 'character_10_yna',\n",
              " 'character_32_patalosaw',\n",
              " 'character_6_cha',\n",
              " 'character_12_thaa',\n",
              " 'digit_4',\n",
              " 'character_29_waw',\n",
              " 'character_34_chhya',\n",
              " 'character_16_tabala',\n",
              " 'character_35_tra',\n",
              " 'character_25_ma',\n",
              " 'digit_8',\n",
              " 'character_18_da',\n",
              " 'character_7_chha',\n",
              " 'character_17_tha',\n",
              " 'character_4_gha',\n",
              " 'digit_1',\n",
              " 'character_30_motosaw',\n",
              " 'character_1_ka',\n",
              " 'character_13_daa']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "# Total number of classes in the dataset\n",
        "classes = os.listdir(train_dir)\n",
        "print(len(classes))\n",
        "classes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nNRYFZi3tP4s"
      },
      "source": [
        "Printing some of the images from the train datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "5tTiitznre0w",
        "outputId": "62862e82-4afd-4ee3-8edd-0f45084e9a52"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "\n",
        "# Load image\n",
        "img = mpimg.imread('/content/Hindi Character Recognition/DevanagariHandwrittenCharacterDataset/Train/character_10_yna/10542.png')\n",
        "\n",
        "# Display image\n",
        "plt.imshow(img)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "2uhpJBjPr2To",
        "outputId": "8bbc3724-680b-4f51-d556-91431681adaa"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Load image\n",
        "img = mpimg.imread('/content/Hindi Character Recognition/DevanagariHandwrittenCharacterDataset/Train/character_10_yna/10576.png')\n",
        "\n",
        "# Display image\n",
        "plt.imshow(img)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "-XgB1fAZsNMI",
        "outputId": "e57c21a4-ec75-4ac0-d469-dca740b3ff0e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Load image\n",
        "img = mpimg.imread('/content/Hindi Character Recognition/DevanagariHandwrittenCharacterDataset/Train/character_20_na/11061.png')\n",
        "\n",
        "# Display image\n",
        "plt.imshow(img)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "sIhexvURsmT4",
        "outputId": "eb826375-5440-413c-c9a3-1c8924b523d7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Load image\n",
        "img = mpimg.imread('/content/Hindi Character Recognition/DevanagariHandwrittenCharacterDataset/Train/character_35_tra/102522.png')\n",
        "\n",
        "# Display image\n",
        "plt.imshow(img)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hindi_character = 'ञ ट ठ ड ढ ण त थ द ध क न प फ ब भ म य र ल व ख श ष स ह ॠ त्र ज्ञ ग घ ङ च छ ज झ ० १ २ ३ ४ ५ ६ ७ ८ ९'.split()   "
      ],
      "metadata": {
        "id": "TW7iCZuPckxC"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "W1vNpiN8tBcw"
      },
      "outputs": [],
      "source": [
        "# Generating Class label\n",
        "# The names of each character, in the correct order for the Hindi Devnagri script\n",
        "class_names='''character_1_ka character_2_kha character_3_ga character_4_gha character_5_kna character_6_cha character_7_chha \n",
        "character_8_ja character_9_jha character_10_yna character_11_taamatar character_12_thaa character_13_daa character_14_dhaa \n",
        "character_15_adna character_16_tabala character_17_tha character_18_da character_19_dha character_20_na character_21_pa \n",
        "character_22_pha character_23_ba character_24_bha character_25_ma character_26_yaw character_27_ra character_28_la \n",
        "character_29_waw character_30_motosaw character_31_petchiryakha character_32_patalosaw character_33_ha character_34_chhya \n",
        "character_35_tra character_36_gya digit_0 digit_1 digit_2 digit_3 digit_4 digit_5 digit_6 digit_7 digit_8 digit_9'''.split()\n",
        "\n",
        "hindi_character = 'क ख ग घ ङ च छ ज झ ञ ट ठ ड ढ ण त थ द ध न प फ ब भ म य र ल व श ष स ह ॠ त्र ज्ञ ० १ २ ३ ४ ५ ६ ७ ८ ९'.split()    "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating Batches\n",
        "\n",
        "#Import the data from the directories, then transform them into batches\n",
        "training_dataset = image_dataset_from_directory(directory=train_dir,\n",
        "                                                image_size=(32,32),\n",
        "                                                batch_size=32,\n",
        "                                                label_mode='categorical')\n",
        "\n",
        "testting_dataset = image_dataset_from_directory(directory=test_dir,\n",
        "                                                image_size=(32,32),\n",
        "                                                batch_size=32,\n",
        "                                                label_mode='categorical',\n",
        "                                                shuffle=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mm3xj1Yov5zx",
        "outputId": "7a36516e-1011-43c3-f87f-203f257ad8ec"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 78200 files belonging to 46 classes.\n",
            "Found 13800 files belonging to 46 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Modelling"
      ],
      "metadata": {
        "id": "mAImQb0mjg0g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Architechture of the CNN\n",
        "\n",
        "model_1 = tf.keras.Sequential([\n",
        "               \n",
        "                tf.keras.Sequential([layers.Rescaling(1./255),]), # # Divide the image array by 255 to normalize it.(Bring the pixel values in the same scale)\n",
        "                #Layer1\n",
        "                layers.Conv2D(filters=6 , kernel_size=(5,5) , activation = 'relu' , input_shape = (32,32,3)),\n",
        "                layers.AveragePooling2D(),\n",
        "\n",
        "                #Layer2\n",
        "                layers.Conv2D(filters=16 , kernel_size=(5,5) , activation = 'relu'),\n",
        "                layers.AveragePooling2D(),\n",
        "                \n",
        "                # Flatten the the array \n",
        "                layers.Flatten(),\n",
        "\n",
        "                #Fully Connected Layer 1\n",
        "                layers.Dense(120 , activation = 'relu'),\n",
        "                #Fully Connected Layer 2\n",
        "                layers.Dense(84 , activation = 'relu'),\n",
        "                #Fully Connected Layer 3\n",
        "                layers.Dense(len(classes) , activation = 'softmax')  # activation = softmax for multi-class classification              \n",
        "])"
      ],
      "metadata": {
        "id": "YWUSXNjPxeq5"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile Model\n",
        "model_1.compile(loss = 'categorical_crossentropy',\n",
        "                optimizer = 'adam',\n",
        "                metrics = ['accuracy'])"
      ],
      "metadata": {
        "id": "xhA8bAAs1FJH"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Model fitting on the loaded dataset\n",
        "model_1_history = model_1.fit(training_dataset,\n",
        "                              validation_data = testting_dataset,\n",
        "                        epochs = 10 ,)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WevBfxkJ7hN4",
        "outputId": "541cc543-eda0-49e5-bfb8-39b0034a7f92"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "2444/2444 [==============================] - 81s 33ms/step - loss: 0.7239 - accuracy: 0.7946 - val_loss: 0.3089 - val_accuracy: 0.9094\n",
            "Epoch 2/10\n",
            "2444/2444 [==============================] - 76s 31ms/step - loss: 0.2434 - accuracy: 0.9262 - val_loss: 0.2031 - val_accuracy: 0.9383\n",
            "Epoch 3/10\n",
            "2444/2444 [==============================] - 71s 29ms/step - loss: 0.1681 - accuracy: 0.9473 - val_loss: 0.1744 - val_accuracy: 0.9456\n",
            "Epoch 4/10\n",
            "2444/2444 [==============================] - 71s 29ms/step - loss: 0.1276 - accuracy: 0.9596 - val_loss: 0.1682 - val_accuracy: 0.9482\n",
            "Epoch 5/10\n",
            "2444/2444 [==============================] - 76s 31ms/step - loss: 0.1057 - accuracy: 0.9660 - val_loss: 0.1499 - val_accuracy: 0.9572\n",
            "Epoch 6/10\n",
            "2444/2444 [==============================] - 82s 34ms/step - loss: 0.0860 - accuracy: 0.9721 - val_loss: 0.1568 - val_accuracy: 0.9572\n",
            "Epoch 7/10\n",
            "2444/2444 [==============================] - 73s 30ms/step - loss: 0.0732 - accuracy: 0.9761 - val_loss: 0.1601 - val_accuracy: 0.9570\n",
            "Epoch 8/10\n",
            "2444/2444 [==============================] - 71s 29ms/step - loss: 0.0628 - accuracy: 0.9792 - val_loss: 0.1384 - val_accuracy: 0.9633\n",
            "Epoch 9/10\n",
            "2444/2444 [==============================] - 72s 30ms/step - loss: 0.0564 - accuracy: 0.9812 - val_loss: 0.1370 - val_accuracy: 0.9654\n",
            "Epoch 10/10\n",
            "2444/2444 [==============================] - 79s 32ms/step - loss: 0.0493 - accuracy: 0.9839 - val_loss: 0.1378 - val_accuracy: 0.9652\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JtuRMmhbCWta"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# graph of validation loss and training data accuracy\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(1, figsize = (10 , 5))\n",
        "plt.plot(model_1_history.history['accuracy'],color='red',label='train')\n",
        "plt.plot(model_1_history.history['val_accuracy'],color='blue',label='validation')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "GLXWhpapuAuV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "674f24b1-80eb-4c23-de26-cbb29eb68713"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model with Batch Normalization and with more convolutional layers"
      ],
      "metadata": {
        "id": "UyOUmJj_-bIk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential([layers.Rescaling(1./255),])\n",
        "\n",
        "#Layer1----------------------------------------------------------\n",
        "model.add(Convolution2D(filters = 32,\n",
        "\t\t\tkernel_size = (3,3),\n",
        "\t\t\tstrides = 1,\n",
        "\t\t\tactivation = \"relu\",\n",
        "\t\t\tinput_shape = (32,32,1)))\n",
        "\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2),\n",
        "\t\t\tstrides=(2, 2),\n",
        "\t\t\tpadding=\"same\"))\n",
        "\n",
        "#Layer2-------------------------------------------------------------\n",
        "model.add(Convolution2D(filters = 32,\n",
        "\t\t\tkernel_size = (3,3),\n",
        "\t\t\tstrides = 1,\n",
        "\t\t\tactivation = \"relu\",\n",
        "\t\t\tinput_shape = (32,32,1)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2),\n",
        "\t\t\tstrides=(2, 2),\n",
        "\t\t\tpadding=\"same\"))\n",
        "\n",
        "\n",
        "#Layers 3-----------------------------------------------------------\t\n",
        "model.add(Convolution2D(filters = 64,\n",
        "\t\t\tkernel_size = (3,3),\n",
        "\t\t\tstrides = 1,\n",
        "\t\t\tactivation = \"relu\"))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2),\n",
        "\t\t\tstrides=(2, 2),\n",
        "\t\t\tpadding=\"same\"))\n",
        "\n",
        "\n",
        "#Layer 4--------------------------------------------------\n",
        "model.add(Convolution2D(filters = 64,\n",
        "\t\t\tkernel_size = (3,3),\n",
        "\t\t\tstrides= 1,\n",
        "\t\t\tactivation = \"relu\"))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2),\n",
        "\t\t\tstrides=(2, 2),\n",
        "\t\t\tpadding=\"same\"))\t\t\t\n",
        "model.add(Flatten())\n",
        "\n",
        "#Fully Connected Layer 1----------------\n",
        "model.add(Dense(128,\n",
        "\t\tactivation = \"relu\",\n",
        "\t\tkernel_initializer = \"uniform\"))\n",
        "model.add(BatchNormalization())\t\t\t\n",
        "\n",
        "#Fully Connected Layer 2----------------\n",
        "model.add(Dense(64,\n",
        "\t\tactivation = \"relu\",\n",
        "\t\tkernel_initializer = \"uniform\"))\n",
        "model.add(BatchNormalization())\t\t\t\n",
        "\n",
        "#Fully Connected Layer 3----------------\n",
        "model.add(Dense(46,\n",
        "\t\tactivation = \"softmax\",\n",
        "\t\tkernel_initializer = \"uniform\"))\t\t\t\n"
      ],
      "metadata": {
        "id": "XH5x6OZ7m9Ad"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile Model\n",
        "model.compile(loss = 'categorical_crossentropy',\n",
        "                optimizer = 'adam',\n",
        "                metrics = ['accuracy'])"
      ],
      "metadata": {
        "id": "-5XE4gAfnUjD"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model fitting on the loaded dataset\n",
        "model_history = model.fit(training_dataset,\n",
        "                              validation_data = testting_dataset,\n",
        "                        epochs = 25 ,)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NwN1Lwg-nYms",
        "outputId": "11693b40-55f5-41fd-dc33-c8bc0b9925c2"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "2444/2444 [==============================] - 45s 12ms/step - loss: 0.5096 - accuracy: 0.8719 - val_loss: 0.1394 - val_accuracy: 0.9577\n",
            "Epoch 2/25\n",
            "2444/2444 [==============================] - 28s 11ms/step - loss: 0.1376 - accuracy: 0.9579 - val_loss: 0.1042 - val_accuracy: 0.9699\n",
            "Epoch 3/25\n",
            "2444/2444 [==============================] - 28s 11ms/step - loss: 0.1028 - accuracy: 0.9683 - val_loss: 0.0951 - val_accuracy: 0.9706\n",
            "Epoch 4/25\n",
            "2444/2444 [==============================] - 28s 12ms/step - loss: 0.0797 - accuracy: 0.9748 - val_loss: 0.0675 - val_accuracy: 0.9794\n",
            "Epoch 5/25\n",
            "2444/2444 [==============================] - 28s 11ms/step - loss: 0.0647 - accuracy: 0.9796 - val_loss: 0.0738 - val_accuracy: 0.9780\n",
            "Epoch 6/25\n",
            "2444/2444 [==============================] - 28s 11ms/step - loss: 0.0586 - accuracy: 0.9817 - val_loss: 0.0688 - val_accuracy: 0.9800\n",
            "Epoch 7/25\n",
            "2444/2444 [==============================] - 28s 12ms/step - loss: 0.0488 - accuracy: 0.9843 - val_loss: 0.0795 - val_accuracy: 0.9787\n",
            "Epoch 8/25\n",
            "2444/2444 [==============================] - 28s 11ms/step - loss: 0.0447 - accuracy: 0.9855 - val_loss: 0.0609 - val_accuracy: 0.9838\n",
            "Epoch 9/25\n",
            "2444/2444 [==============================] - 28s 11ms/step - loss: 0.0405 - accuracy: 0.9870 - val_loss: 0.0675 - val_accuracy: 0.9822\n",
            "Epoch 10/25\n",
            "2444/2444 [==============================] - 30s 12ms/step - loss: 0.0352 - accuracy: 0.9885 - val_loss: 0.0703 - val_accuracy: 0.9813\n",
            "Epoch 11/25\n",
            "2444/2444 [==============================] - 28s 11ms/step - loss: 0.0304 - accuracy: 0.9901 - val_loss: 0.0760 - val_accuracy: 0.9802\n",
            "Epoch 12/25\n",
            "2444/2444 [==============================] - 28s 11ms/step - loss: 0.0314 - accuracy: 0.9896 - val_loss: 0.0689 - val_accuracy: 0.9822\n",
            "Epoch 13/25\n",
            "2444/2444 [==============================] - 30s 12ms/step - loss: 0.0271 - accuracy: 0.9909 - val_loss: 0.0647 - val_accuracy: 0.9836\n",
            "Epoch 14/25\n",
            "2444/2444 [==============================] - 28s 11ms/step - loss: 0.0262 - accuracy: 0.9913 - val_loss: 0.0657 - val_accuracy: 0.9849\n",
            "Epoch 15/25\n",
            "2444/2444 [==============================] - 28s 11ms/step - loss: 0.0223 - accuracy: 0.9928 - val_loss: 0.0610 - val_accuracy: 0.9846\n",
            "Epoch 16/25\n",
            "2444/2444 [==============================] - 31s 12ms/step - loss: 0.0239 - accuracy: 0.9919 - val_loss: 0.0618 - val_accuracy: 0.9862\n",
            "Epoch 17/25\n",
            "2444/2444 [==============================] - 28s 11ms/step - loss: 0.0219 - accuracy: 0.9930 - val_loss: 0.0656 - val_accuracy: 0.9843\n",
            "Epoch 18/25\n",
            "2444/2444 [==============================] - 28s 11ms/step - loss: 0.0186 - accuracy: 0.9940 - val_loss: 0.0683 - val_accuracy: 0.9841\n",
            "Epoch 19/25\n",
            "2444/2444 [==============================] - 28s 12ms/step - loss: 0.0210 - accuracy: 0.9932 - val_loss: 0.0646 - val_accuracy: 0.9841\n",
            "Epoch 20/25\n",
            "2444/2444 [==============================] - 28s 11ms/step - loss: 0.0193 - accuracy: 0.9937 - val_loss: 0.0608 - val_accuracy: 0.9862\n",
            "Epoch 21/25\n",
            "2444/2444 [==============================] - 27s 11ms/step - loss: 0.0176 - accuracy: 0.9939 - val_loss: 0.0817 - val_accuracy: 0.9807\n",
            "Epoch 22/25\n",
            "2444/2444 [==============================] - 28s 11ms/step - loss: 0.0177 - accuracy: 0.9940 - val_loss: 0.0524 - val_accuracy: 0.9871\n",
            "Epoch 23/25\n",
            "2444/2444 [==============================] - 31s 13ms/step - loss: 0.0153 - accuracy: 0.9951 - val_loss: 0.0573 - val_accuracy: 0.9865\n",
            "Epoch 24/25\n",
            "2444/2444 [==============================] - 28s 11ms/step - loss: 0.0155 - accuracy: 0.9950 - val_loss: 0.0654 - val_accuracy: 0.9859\n",
            "Epoch 25/25\n",
            "2444/2444 [==============================] - 28s 11ms/step - loss: 0.0154 - accuracy: 0.9947 - val_loss: 0.0672 - val_accuracy: 0.9840\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Predicting some Test images"
      ],
      "metadata": {
        "id": "k_TuvzCQjb8S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# predictiting character 22_pha\n",
        "import cv2\n",
        "test_img = cv2.imread(\"/content/Hindi Character Recognition/DevanagariHandwrittenCharacterDataset/Test/character_22_pha/11175.png\")\n",
        "plt.imshow(test_img)\n",
        "test_img = cv2.resize(test_img,(32,32))\n",
        "test_input = test_img.reshape((1,32,32,3))\n",
        "model.predict(test_input)\n",
        "predicted_probability = model.predict(test_input , verbose=1)\n",
        "predicted_class = predicted_probability.argmax(axis=1)\n",
        "class_number = predicted_class[0]\n",
        "print(\"Actual_class : फ\")\n",
        "print(\"Predicted Class : \" ,hindi_character[class_number])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "JX0auP6nib8W",
        "outputId": "9a47171c-d096-4d9e-9ebe-1f98119a8922"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "Actual_class : फ\n",
            "Predicted Class :  फ\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_img_2 = test_img = cv2.imread(\"/content/Hindi Character Recognition/DevanagariHandwrittenCharacterDataset/Test/digit_3/18830.png\")\n",
        "plt.imshow(test_img_2)\n",
        "test_img_2 = cv2.resize(test_img_2,(32,32))\n",
        "test_input = test_img_2.reshape((1,32,32,3))\n",
        "model.predict(test_input)\n",
        "predicted_probability = model.predict(test_input , verbose=1)\n",
        "predicted_class = predicted_probability.argmax(axis=1)\n",
        "class_number = predicted_class[0]\n",
        "print(\"Actual_class : ३\")\n",
        "print(\"Predicted Class : \" ,hindi_character[class_number])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "U8syDiwOoUn9",
        "outputId": "07041f07-2a99-4481-913e-ca3728a06bdd"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "Actual_class : ३\n",
            "Predicted Class :  ३\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_img_2 = test_img = cv2.imread(\"/content/Hindi Character Recognition/DevanagariHandwrittenCharacterDataset/Test/digit_5/13547.png\")\n",
        "plt.imshow(test_img_2)\n",
        "test_img_2 = cv2.resize(test_img_2,(32,32))\n",
        "test_input = test_img_2.reshape((1,32,32,3))\n",
        "model.predict(test_input)\n",
        "predicted_probability = model.predict(test_input , verbose=1)\n",
        "predicted_class = predicted_probability.argmax(axis=1)\n",
        "class_number = predicted_class[0]\n",
        "print(\"Actual_class : ५\")\n",
        "print(\"Predicted Class : \" ,hindi_character[class_number])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "BoUMxwhchwRp",
        "outputId": "5fe21019-03b3-485f-8247-afcb4238f941"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "Actual_class : ५\n",
            "Predicted Class :  ५\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_img_2 = test_img = cv2.imread(\"/content/Hindi Character Recognition/DevanagariHandwrittenCharacterDataset/Test/character_32_patalosaw/23077.png\")\n",
        "plt.imshow(test_img_2)\n",
        "test_img_2 = cv2.resize(test_img_2,(32,32))\n",
        "test_input = test_img_2.reshape((1,32,32,3))\n",
        "model.predict(test_input)\n",
        "predicted_probability = model.predict(test_input , verbose=1)\n",
        "predicted_class = predicted_probability.argmax(axis=1)\n",
        "class_number = predicted_class[0]\n",
        "print(\"Actual_class : स\")\n",
        "print(\"Predicted Class : \" ,hindi_character[class_number])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "VRsqUOzDh_OB",
        "outputId": "2eb3ae11-cecc-4d09-b7e6-fbd75cb80aa6"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "Actual_class : स\n",
            "Predicted Class :  स\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_img_2 = test_img = cv2.imread(\"/content/Hindi Character Recognition/DevanagariHandwrittenCharacterDataset/Test/character_20_na/13017.png\")\n",
        "plt.imshow(test_img_2)\n",
        "test_img_2 = cv2.resize(test_img_2,(32,32))\n",
        "test_input = test_img_2.reshape((1,32,32,3))\n",
        "model.predict(test_input)\n",
        "predicted_probability = model.predict(test_input , verbose=1)\n",
        "predicted_class = predicted_probability.argmax(axis=1)\n",
        "class_number = predicted_class[0]\n",
        "print(\"Actual_class : न\")\n",
        "print(\"Predicted Class : \" ,hindi_character[class_number])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "55riIxfUiYWJ",
        "outputId": "28263af3-a81b-418d-db48-4c5b73fa4f8d"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "Actual_class : न\n",
            "Predicted Class :  न\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_img_2 = test_img = cv2.imread(\"/content/Hindi Character Recognition/DevanagariHandwrittenCharacterDataset/Test/character_23_ba/17496.png\")\n",
        "plt.imshow(test_img_2)\n",
        "test_img_2 = cv2.resize(test_img_2,(32,32))\n",
        "test_input = test_img_2.reshape((1,32,32,3))\n",
        "model.predict(test_input)\n",
        "predicted_probability = model.predict(test_input , verbose=1)\n",
        "predicted_class = predicted_probability.argmax(axis=1)\n",
        "class_number = predicted_class[0]\n",
        "print(\"Actual_class :  ब\")\n",
        "print(\"Predicted Class : \" ,hindi_character[class_number])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "ocZcmnxTit4B",
        "outputId": "c9e2da86-dffc-46b6-b731-b6e413a2a870"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 17ms/step\n",
            "Actual_class :  ब\n",
            "Predicted Class :  ब\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}