Handwritten_word_Character_recognition.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Crinmatic/Word-level-Optical-Recognition-on-Yoruba-language/blob/main/Handwritten_word_Character_recognition.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1cdqzmf9F-4_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d660e80-c672-41ef-9d78-6087d66c527e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tensorflow-addons\n",
            "  Downloading tensorflow_addons-0.17.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 5.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from tensorflow-addons) (21.3)\n",
            "Requirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow-addons) (2.7.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->tensorflow-addons) (3.0.9)\n",
            "Installing collected packages: tensorflow-addons\n",
            "Successfully installed tensorflow-addons-0.17.0\n"
          ]
        }
      ],
      "source": [
        "!pip install -U tensorflow-addons\n",
        "import cv2\n",
        "import os\n",
        "import imutils\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from google.colab.patches import cv2_imshow\n",
        "import random\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from keras.utils import np_utils\n",
        "from keras.models import Sequential\n",
        "from keras import optimizers\n",
        "from keras import backend as K\n",
        "from keras.layers import Dense, Activation, Flatten, Dense,MaxPooling2D, Dropout\n",
        "from keras.layers import Conv2D, MaxPooling2D, BatchNormalization\n",
        "from keras import Input, Model\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "import tensorflow_addons as tfa\n",
        "from keras.models import Sequential, load_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9goSGZL6L0SW"
      },
      "outputs": [],
      "source": [
        "dir = \"/content/drive/MyDrive/Datasets/word level  yoruba handwritten TRAINING DATASET/Letters_Data\"\n",
        "train_data = []\n",
        "img_size = 32\n",
        "non_chars = [\"#\",\"$\",\"&\",\"@\"]\n",
        "for i in os.listdir(dir):\n",
        "    if i in non_chars:\n",
        "        continue\n",
        "    count = 0\n",
        "    sub_directory = os.path.join(dir,i)\n",
        "    for j in os.listdir(sub_directory):\n",
        "        count+=1\n",
        "        if count > 4000:\n",
        "            break\n",
        "        img = cv2.imread(os.path.join(sub_directory,j), 0)\n",
        "        img = cv2.resize(img,(img_size,img_size))\n",
        "        thresh = cv2.threshold(img, 0, 255,cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]\n",
        "        blurred = cv2.GaussianBlur(thresh, (5, 5), 0)\n",
        "        train_data.append([blurred,i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70WMGF0k-SfM",
        "outputId": "47ab1305-f8fc-498a-f5c7-03d94ca62d4e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8820"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "len(train_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3iJ0arkQKyQe",
        "outputId": "e7c2deff-30c5-4284-f1dd-3bdcdf7b279c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "         [0, 0, 1, ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
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              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
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              "         ...,\n",
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              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
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              "         ...,\n",
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              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
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              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[  0,   0,   0, ...,   0,   0,   0],\n",
              "         [  0,   0,   0, ...,   0,   0,   0],\n",
              "         [  0,   0,   0, ...,   0,   0,   0],\n",
              "         ...,\n",
              "         [ 80,  80,  80, ...,  69,  65,  64],\n",
              "         [175, 175, 175, ..., 131, 116, 112],\n",
              "         [223, 223, 223, ..., 157, 133, 128]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
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              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[  0,   0,   0, ...,   0,   0,   0],\n",
              "         [  0,   0,   0, ...,   0,   0,   0],\n",
              "         [  0,   0,   0, ...,   0,   0,   0],\n",
              "         ...,\n",
              "         [159, 159, 159, ...,  20,  16,  16],\n",
              "         [175, 175, 175, ...,  71,  64,  64],\n",
              "         [159, 159, 159, ..., 104,  96,  96]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
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              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
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              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
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              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
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              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
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              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
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              "         ...,\n",
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              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
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              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
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              "         ...,\n",
              "         [0, 0, 5, ..., 0, 0, 0],\n",
              "         [0, 0, 1, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
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              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
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              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
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              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
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              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
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              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
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              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
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              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " [array([[0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0],\n",
              "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), 'G'],\n",
              " ...]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "train_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zjFvFYUq-oVf"
      },
      "outputs": [],
      "source": [
        "random.shuffle(train_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZDxYW5m7-5_Y"
      },
      "outputs": [],
      "source": [
        "X_train = []\n",
        "y_train = []\n",
        "for features,label in train_data:\n",
        "    X_train.append(features)\n",
        "    y_train.append(label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FNMu1lZS_LRI"
      },
      "outputs": [],
      "source": [
        "LB = LabelBinarizer()\n",
        "y_train = LB.fit_transform(y_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Y6mj_wXSyFD",
        "outputId": "246e42f8-9959-422d-d23d-95779a19b9c1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       ...,\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "y_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rcXfKuG3ASSH"
      },
      "outputs": [],
      "source": [
        "X_train = np.asarray(X_train)/255.0\n",
        "X_train = X_train.reshape(-1,32,32,1)\n",
        "\n",
        "y_train = np.array(y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dj1NrFhLDl8Y",
        "outputId": "2eea9568-96ed-429d-a0de-febc3aa1e85a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8820, 32, 32, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "X_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EBToWCQfhM2i"
      },
      "outputs": [],
      "source": [
        "learning_rate = 0.001\n",
        "weight_decay = 0.0001\n",
        "batch_size = 256\n",
        "num_epochs = 20\n",
        "image_size = 72  # We'll resize input images to this size\n",
        "patch_size = 6  # Size of the patches to be extract from the input images\n",
        "num_patches = (image_size // patch_size) ** 2\n",
        "projection_dim = 64\n",
        "num_heads = 4\n",
        "transformer_units = [\n",
        "    projection_dim * 2,\n",
        "    projection_dim,\n",
        "]  # Size of the transformer layers\n",
        "transformer_layers = 8\n",
        "mlp_head_units = [2048, 1024]  # Size of the dense layers of the final classifier\n",
        "num_classes = 60\n",
        "input_shape = (32, 32, 1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M_Ymd5vi9WUH"
      },
      "outputs": [],
      "source": [
        "data_augmentation = keras.Sequential(\n",
        "    [\n",
        "        layers.Normalization(),\n",
        "        layers.Resizing(image_size, image_size)\n",
        "    ],\n",
        "    name=\"data_augmentation\",\n",
        ")\n",
        "# Compute the mean and the variance of the training data for normalization.\n",
        "data_augmentation.layers[0].adapt(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iU23UJL2iPTo"
      },
      "outputs": [],
      "source": [
        "def mlp(x, hidden_units, dropout_rate):\n",
        "    for units in hidden_units:\n",
        "        x = layers.Dense(units, activation=tf.nn.gelu)(x)\n",
        "        x = layers.Dropout(dropout_rate)(x)\n",
        "    return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0LmhQVeOiRFg"
      },
      "outputs": [],
      "source": [
        "class PatchEncoder(layers.Layer):\n",
        "    def __init__(self, num_patches, projection_dim):\n",
        "        super(PatchEncoder, self).__init__()\n",
        "        self.num_patches = num_patches\n",
        "        self.projection = layers.Dense(units=projection_dim)\n",
        "        self.position_embedding = layers.Embedding(\n",
        "            input_dim=num_patches, output_dim=projection_dim\n",
        "        )\n",
        "\n",
        "    # Override function to avoid error while saving model\n",
        "    def get_config(self):\n",
        "        config = super().get_config().copy()\n",
        "        config.update(\n",
        "            {\n",
        "                \"input_shape\": input_shape,\n",
        "                \"patch_size\": patch_size,\n",
        "                \"num_patches\": num_patches,\n",
        "                \"projection_dim\": projection_dim,\n",
        "                \"num_heads\": num_heads,\n",
        "                \"transformer_units\": transformer_units,\n",
        "                \"transformer_layers\": transformer_layers,\n",
        "                \"mlp_head_units\": mlp_head_units,\n",
        "            }\n",
        "        )\n",
        "        return config\n",
        "\n",
        "    def call(self, patch):\n",
        "        positions = tf.range(start=0, limit=self.num_patches, delta=1)\n",
        "        encoded = self.projection(patch) + self.position_embedding(positions)\n",
        "        return encoded"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ITmxG6thySoD"
      },
      "outputs": [],
      "source": [
        "class Patches(layers.Layer):\n",
        "    def __init__(self, patch_size):\n",
        "        super(Patches, self).__init__()\n",
        "        self.patch_size = patch_size\n",
        "\n",
        "    def call(self, images):\n",
        "        batch_size = tf.shape(images)[0]\n",
        "        patches = tf.image.extract_patches(\n",
        "            images=images,\n",
        "            sizes=[1, self.patch_size, self.patch_size, 1],\n",
        "            strides=[1, self.patch_size, self.patch_size, 1],\n",
        "            rates=[1, 1, 1, 1],\n",
        "            padding=\"VALID\",\n",
        "        )\n",
        "        patch_dims = patches.shape[-1]\n",
        "        patches = tf.reshape(patches, [batch_size, -1, patch_dims])\n",
        "        return patches"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q_FVf7psiVx-"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(4, 4))\n",
        "image = X_train[np.random.choice(range(X_train.shape[0]))]\n",
        "\n",
        "plt.imshow(image)\n",
        "plt.axis(\"off\")\n",
        "\n",
        "resized_image = tf.image.resize(\n",
        "    tf.convert_to_tensor([image]), size=(image_size, image_size)\n",
        ")\n",
        "patches = Patches(patch_size)(resized_image)\n",
        "print(f\"Image size: {image_size} X {image_size}\")\n",
        "print(f\"Patch size: {patch_size} X {patch_size}\")\n",
        "print(f\"Patches per image: {patches.shape[1]}\")\n",
        "print(f\"Elements per patch: {patches.shape[-1]}\")\n",
        "\n",
        "n = int(np.sqrt(patches.shape[1]))\n",
        "plt.figure(figsize=(4, 4))\n",
        "for i, patch in enumerate(patches[0]):\n",
        "    ax = plt.subplot(n, n, i + 1)\n",
        "    patch_img = tf.reshape(patch, (patch_size, patch_size, 1))\n",
        "    plt.imshow(patch_img.numpy().astype(\"uint8\"))\n",
        "    plt.axis(\"off\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rK-x6Kt-nX0j"
      },
      "outputs": [],
      "source": [
        "class PatchEncoder(layers.Layer):\n",
        "    def __init__(self, num_patches, projection_dim):\n",
        "        super(PatchEncoder, self).__init__()\n",
        "        self.num_patches = num_patches\n",
        "        self.projection = layers.Dense(units=projection_dim)\n",
        "        self.position_embedding = layers.Embedding(\n",
        "            input_dim=num_patches, output_dim=projection_dim\n",
        "        )\n",
        "     # Override function to avoid error while saving model\n",
        "    def get_config(self):\n",
        "        config = super().get_config().copy()\n",
        "        config.update(\n",
        "            {\n",
        "                \"input_shape\": input_shape,\n",
        "                \"patch_size\": patch_size,\n",
        "                \"num_patches\": num_patches,\n",
        "                \"projection_dim\": projection_dim,\n",
        "                \"num_heads\": num_heads,\n",
        "                \"transformer_units\": transformer_units,\n",
        "                \"transformer_layers\": transformer_layers,\n",
        "                \"mlp_head_units\": mlp_head_units,\n",
        "            }\n",
        "        )\n",
        "        return config\n",
        "    def call(self, patch):\n",
        "        positions = tf.range(start=0, limit=self.num_patches, delta=1)\n",
        "        encoded = self.projection(patch) + self.position_embedding(positions)\n",
        "        return encoded"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KN8W2Tu2neTl"
      },
      "outputs": [],
      "source": [
        "inputs = layers.Input(shape=input_shape)\n",
        "    # Augment data.\n",
        "augmented = data_augmentation(inputs)\n",
        "    # Create patches.\n",
        "patches = Patches(patch_size)(augmented)\n",
        "    # Encode patches.\n",
        "encoded_patches = PatchEncoder(num_patches, projection_dim)(patches)\n",
        "\n",
        "    # Create multiple layers of the Transformer block.\n",
        "for _ in range(transformer_layers):\n",
        "        # Layer normalization 1.\n",
        "        x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)\n",
        "        # Create a multi-head attention layer.\n",
        "        attention_output = layers.MultiHeadAttention(\n",
        "            num_heads=num_heads, key_dim=projection_dim, dropout=0.1\n",
        "        )(x1, x1)\n",
        "        # Skip connection 1.\n",
        "        x2 = layers.Add()([attention_output, encoded_patches])\n",
        "        # Layer normalization 2.\n",
        "        x3 = layers.LayerNormalization(epsilon=1e-6)(x2)\n",
        "        # MLP.\n",
        "        x3 = mlp(x3, hidden_units=transformer_units, dropout_rate=0.1)\n",
        "        # Skip connection 2.\n",
        "        encoded_patches = layers.Add()([x3, x2])\n",
        "\n",
        "    # Create a [batch_size, projection_dim] tensor.\n",
        "representation = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)\n",
        "representation = layers.Flatten()(representation)\n",
        "representation = layers.Dropout(0.5)(representation)\n",
        "    # Add MLP.\n",
        "features = mlp(representation, hidden_units=mlp_head_units, dropout_rate=0.5)\n",
        "    # Classify outputs.\n",
        "logits = layers.Dense(num_classes)(features)\n",
        "    # Create the Keras model.\n",
        "model = keras.Model(inputs=inputs, outputs=logits)\n",
        "   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n4I87nkonmcO",
        "outputId": "4c64b664-a6d1-4c17-ad2e-95a5b2110afa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "28/28 [==============================] - 447s 15s/step - loss: 4.4457 - accuracy: 0.0565 - top-5-accuracy: 0.2014 - val_loss: 3.2607 - val_accuracy: 0.2319 - val_top-5-accuracy: 0.5278\n",
            "Epoch 2/20\n",
            "28/28 [==============================] - 438s 16s/step - loss: 3.2061 - accuracy: 0.1908 - top-5-accuracy: 0.4887 - val_loss: 2.4565 - val_accuracy: 0.3719 - val_top-5-accuracy: 0.7171\n",
            "Epoch 3/20\n",
            "28/28 [==============================] - 438s 16s/step - loss: 2.7400 - accuracy: 0.2785 - top-5-accuracy: 0.6213 - val_loss: 2.1416 - val_accuracy: 0.4286 - val_top-5-accuracy: 0.7664\n",
            "Epoch 4/20\n",
            "28/28 [==============================] - 441s 16s/step - loss: 2.4130 - accuracy: 0.3468 - top-5-accuracy: 0.7031 - val_loss: 1.9110 - val_accuracy: 0.4637 - val_top-5-accuracy: 0.8141\n",
            "Epoch 5/20\n",
            "28/28 [==============================] - 432s 15s/step - loss: 2.1888 - accuracy: 0.3845 - top-5-accuracy: 0.7557 - val_loss: 1.7442 - val_accuracy: 0.4892 - val_top-5-accuracy: 0.8424\n",
            "Epoch 6/20\n",
            "28/28 [==============================] - 436s 16s/step - loss: 1.9269 - accuracy: 0.4437 - top-5-accuracy: 0.8112 - val_loss: 1.5690 - val_accuracy: 0.5261 - val_top-5-accuracy: 0.8787\n",
            "Epoch 7/20\n",
            "28/28 [==============================] - 439s 16s/step - loss: 1.7353 - accuracy: 0.4897 - top-5-accuracy: 0.8478 - val_loss: 1.3945 - val_accuracy: 0.5726 - val_top-5-accuracy: 0.9031\n",
            "Epoch 8/20\n",
            "28/28 [==============================] - 433s 15s/step - loss: 1.5203 - accuracy: 0.5334 - top-5-accuracy: 0.8805 - val_loss: 1.3041 - val_accuracy: 0.5862 - val_top-5-accuracy: 0.9133\n",
            "Epoch 9/20\n",
            "28/28 [==============================] - 441s 16s/step - loss: 1.3809 - accuracy: 0.5673 - top-5-accuracy: 0.9094 - val_loss: 1.2044 - val_accuracy: 0.6060 - val_top-5-accuracy: 0.9195\n",
            "Epoch 10/20\n",
            "28/28 [==============================] - 440s 16s/step - loss: 1.2472 - accuracy: 0.5962 - top-5-accuracy: 0.9253 - val_loss: 1.1545 - val_accuracy: 0.6162 - val_top-5-accuracy: 0.9354\n",
            "Epoch 11/20\n",
            "28/28 [==============================] - 447s 16s/step - loss: 1.1118 - accuracy: 0.6361 - top-5-accuracy: 0.9386 - val_loss: 1.0666 - val_accuracy: 0.6440 - val_top-5-accuracy: 0.9473\n",
            "Epoch 12/20\n",
            "28/28 [==============================] - 436s 16s/step - loss: 0.9972 - accuracy: 0.6719 - top-5-accuracy: 0.9521 - val_loss: 1.0482 - val_accuracy: 0.6440 - val_top-5-accuracy: 0.9501\n",
            "Epoch 13/20\n",
            "28/28 [==============================] - 451s 16s/step - loss: 0.9308 - accuracy: 0.6950 - top-5-accuracy: 0.9623 - val_loss: 1.0033 - val_accuracy: 0.6593 - val_top-5-accuracy: 0.9535\n",
            "Epoch 14/20\n",
            "28/28 [==============================] - 444s 16s/step - loss: 0.8289 - accuracy: 0.7141 - top-5-accuracy: 0.9734 - val_loss: 0.9677 - val_accuracy: 0.6582 - val_top-5-accuracy: 0.9535\n",
            "Epoch 15/20\n",
            "28/28 [==============================] - 447s 16s/step - loss: 0.7626 - accuracy: 0.7432 - top-5-accuracy: 0.9743 - val_loss: 0.9638 - val_accuracy: 0.6616 - val_top-5-accuracy: 0.9580\n",
            "Epoch 16/20\n",
            "28/28 [==============================] - 454s 16s/step - loss: 0.7122 - accuracy: 0.7541 - top-5-accuracy: 0.9790 - val_loss: 0.9455 - val_accuracy: 0.6661 - val_top-5-accuracy: 0.9575\n",
            "Epoch 17/20\n",
            "28/28 [==============================] - 453s 16s/step - loss: 0.6585 - accuracy: 0.7701 - top-5-accuracy: 0.9834 - val_loss: 0.9347 - val_accuracy: 0.6859 - val_top-5-accuracy: 0.9592\n",
            "Epoch 18/20\n",
            "28/28 [==============================] - 453s 16s/step - loss: 0.6334 - accuracy: 0.7766 - top-5-accuracy: 0.9844 - val_loss: 0.9414 - val_accuracy: 0.6735 - val_top-5-accuracy: 0.9626\n",
            "Epoch 19/20\n",
            "28/28 [==============================] - 445s 16s/step - loss: 0.5800 - accuracy: 0.7973 - top-5-accuracy: 0.9872 - val_loss: 0.9295 - val_accuracy: 0.6837 - val_top-5-accuracy: 0.9620\n",
            "Epoch 20/20\n",
            "28/28 [==============================] - 449s 16s/step - loss: 0.5306 - accuracy: 0.8068 - top-5-accuracy: 0.9872 - val_loss: 0.9527 - val_accuracy: 0.6910 - val_top-5-accuracy: 0.9615\n"
          ]
        }
      ],
      "source": [
        "\n",
        "optimizer = tfa.optimizers.AdamW(\n",
        "        learning_rate=learning_rate, weight_decay=weight_decay\n",
        "    )\n",
        "\n",
        "model.compile(\n",
        "        optimizer=optimizer,\n",
        "        loss=keras.losses.CategoricalCrossentropy(from_logits=True),\n",
        "        metrics=[\n",
        "            keras.metrics.CategoricalAccuracy(name=\"accuracy\"),\n",
        "            keras.metrics.TopKCategoricalAccuracy(5, name=\"top-5-accuracy\"),\n",
        "        ],\n",
        "    )\n",
        "\n",
        "checkpoint_filepath = \"/content/drive/MyDrive/Datasets/word level  yoruba handwritten TRAINING DATASET/tmp/checkpoint\"\n",
        "checkpoint_callback = keras.callbacks.ModelCheckpoint(\n",
        "        checkpoint_filepath,\n",
        "        monitor=\"val_accuracy\",\n",
        "        save_best_only=True,\n",
        "        save_weights_only=True,\n",
        "    )\n",
        "\n",
        "history = model.fit(\n",
        "        x= X_train,\n",
        "        y= y_train,\n",
        "        batch_size=batch_size,\n",
        "        epochs=num_epochs,\n",
        "        validation_split=0.2,\n",
        "        callbacks=[checkpoint_callback],\n",
        "    )\n",
        "    \n",
        "    \n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LwsbfttzFwim"
      },
      "outputs": [],
      "source": [
        "checkpoint_filepath = \"/content/drive/MyDrive/Datasets/word level  yoruba handwritten TRAINING DATASET/tmp/checkpoint\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_usqfV6FD1Xo"
      },
      "outputs": [],
      "source": [
        "model.load_weights(checkpoint_filepath)\n",
        "_, accuracy, top_5_accuracy = model.evaluate(train_X, train_Y)\n",
        "print(f\"Test accuracy: {round(accuracy * 100, 2)}%\")\n",
        "print(f\"Test top 5 accuracy: {round(top_5_accuracy * 100, 2)}%\")\n",
        "c = model.predict(train_X)\n",
        "print(\"result:\", c)\n",
        "pred = LB.inverse_transform(c)\n",
        "print(pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lBpdJcibUE4D"
      },
      "outputs": [],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "vcxUUhCMcKtt",
        "outputId": "5bb743ad-b97b-45b7-fddb-79d40d73cce9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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fuREjujdlcPsYatXw5oSulzw9R6Cqs3EuCfWddp/P8HpgoJcxVBZ//OMfGT9+PH//+9+5+OKLAx2OMaYMeQWF7MnKJS3jCGkZuezKOMJP6dl8vn43aZk5hNcM4ZyOcYzo3pShHeKoHVb1dv6+qlyfxYmJiVq8Y5oNGzbQqVOnAEVUfdl2NdXR4dx8dmXksDszh7SMHNLc56JpuzJySD+cS/FdY+2aoZzVLoYR3ZtwXqdGRNQK9LU2J0dElqtqideqV613YowxJyHjSB7fJ6ezKGkfi7fuJ/XAEbJyf3nerX6dmjSuG07jeuF0aVqXxvXCaVw3nEb1wmniDterXbPaNr1iicAYU23k5BWw4qcDfJO0j2+3pLMm5SCF6vyb79uqIWe0jj62kz+2s68bXuWrdk6XJQJjTJVVUKis25nBN0n7WJSUztJt+8nNLyQ0ROjVvD6TzmnHWW1j6Nm8PmE1qsYVPIFgicAYU2WoKlv3HebbpH18m5TOd8npZBxx7snp2DiKa/u35Kx20fRrFU1kFavDDyTbUsaYSmv/4aNs3JXJxrQs1u7M4Lst6ezKcPrJaFa/Nhd2acTAtjEMaBNDbFStEyzNlMYSgTEm4I7mF7Jl7yE2pmWycVcWG9Ky2Lgrkz1ZucfKxESG0a9VQya1jWFgmxhaRteptidv/c0SQQUYOnQokydP5sILLzw27cknn2TTpk0899xzvyg/ZMgQHn/8cRITE7nooot48803qV+//s/KlNSKaXEzZ86kffv2dO7cGYD77ruPwYMHc955JbbdZ0zAqSq7M3PZ4O7wN6U5//aT9hwiv9C5XjMsNIR2jSIZ1C6WTk2i6Ni4Lh0aR9k/fg9ZIqgAY8eOZfr06T9LBNOnT+fRRx894Wtnz559wjKlmTlzJiNGjDiWCB588MFTXpYxXiksVD5bv5tpi39iTWoGB7OPt7PVtF44HZvU5ZyOcXRsUpdOjaNIiImoMk0zVBe2tSvAFVdcwSeffMLRo0cB2LZtGzt37uStt94iMTGRLl268Le//a3E1yYkJLBv3z4AHn74Ydq3b89ZZ53Fpk2bjpV56aWX6Nu3Lz169ODyyy8nOzubRYsWMWvWLO6++2569uzJli1bmDBhAu+++y4AX3zxBb169aJbt27ccMMN5ObmHlvf3/72N3r37k23bt3YuHGjl5vGBLG8gkLeX5HCBU8u4OY3lrMt/TDDuzbmgZFdmHHTGfxw3wUsuudcXpnQlz8O68jIHk1p1yjKkkAAVL8jgjmTIW1NxS6zcTcY/kipsxs2bEi/fv2YM2cOo0aNYvr06Vx11VX8+c9/pmHDhhQUFHDuueeyevVqunfvXuIyli9fzvTp01m1ahX5+fn07t2bPn36ADB69GgmTpwIwL333svLL7/MbbfdxsiRIxkxYgRXXHHFz5aVk5PDhAkT+OKLL2jfvj3jxo3jueee48477wQgJiaGFStW8Oyzz/L4448zZcqUithKxgDOtfzvLk/h+a+3kHLgCB0bR/H02F5c1LVxpW6BM5jZp1JBiqqHwKkWGjt2LG+//Ta9e/emV69erFu3jvXr15f6+oULF3LZZZdRp04d6taty8iRI4/NW7t2LYMGDaJbt25MmzaNdevWlRnLpk2baNWqFe3btwdg/PjxLFiw4Nj80aNHA9CnTx+2bdt2qm/ZmJ85nJvPiwu2MOjR+dw7cy0xkbWYMi6R2bcPYmSPppYEKrHqd0RQxj93L40aNYq77rqLFStWkJ2dTcOGDXn88cdZunQpDRo0YMKECeTk5JzSsidMmMDMmTPp0aMHU6dO5auvvjqtWIuaurZmrk1FOJh9lKmLtjF10TYOZucxsG00T13dkzPbRNtVPVWEpegKEhkZydChQ7nhhhsYO3YsmZmZREREUK9ePXbv3s2cOXPKfP3gwYOZOXMmR44cISsri48++ujYvKysLJo0aUJeXh7Tpk07Nj0qKoqsrKxfLKtDhw5s27aNpKQkAP73v/9x9tlnV9A7NcaxJzOHf87ewMBHvuTJeZtJbNmQ9387gGm/PoMBbWMsCVQh1e+IIIDGjh3LZZddxvTp0+nYsSO9evWiY8eONG/enIEDy25tu3fv3lx99dX06NGDuLg4+vbte2zeQw89RP/+/YmNjaV///7Hdv5jxoxh4sSJPP3008dOEgOEh4fz6quvcuWVV5Kfn0/fvn25+eabvXnTJujs2J/NCwu28PayFPILCrmkR1NuGdKGjo3rBjo0c4qsGWpTKtuuxlfSniye/WoLH67aSYjAFX3i+c3gNiTERJz4xSbgrBlqY8xJU1U27znE3LVpfLZ+N2tSMwivGcL4MxOYOLgVTerVDnSIpoJ4mghEZBjwFE5XlVNU9ZFi8/8DDHVH6wBxqvrzW2yNMX5TWKis3HGQz9alMXddGtvSswHo1aI+k4d35Mo+8URH2h2+1Y1niUBEQoFngPOBFGCpiMxyu6cEQFXv8il/G9DrVNenqnZyqgJVtSpDc+qO5heyaMs+Plu/m8/X72ZvVi41Q4Uz28Tw60GtuaBzI+Lqhgc6TOMhL48I+gFJqpoMICLTgVFAaRfTjwVKvv32BMLDw0lPTyc62i5XqwiqSnp6OuHh9uOvrg7l5vPVpj3MXbebrzbuISs3nzphoQztEMcFXRoxpEMc9WrXDHSYxk+8TATNgB0+4ylA/5IKikhLoBXwZSnzbwJuAmjRosUv5sfHx5OSksLevXtPM2RTJDw8nPj4+ECHYSrQ3qxcvtiwm7nr0vg2KZ2jBYVER4RxUbcmXNi1EQPaxBBeM7h76gpWleVk8RjgXVUtKGmmqr4IvAjOVUPF59esWZNWrVp5G6ExVVBufgHz1u/hneU7WPDjXgoV4hvU5rozW3Jhl8b0admA0BA7iq4SCgucR42wCl+0l4kgFWjuMx7vTivJGOBWD2MxJqisTc3gnWU7+PCHnRzMzqNJvXBuGdKGi7s1pVOTKKtCrYzycyEjBQ5uh4wdcHCHz/N2yNwJI56E3tdV+Kq9TARLgXYi0gonAYwBrileSEQ6Ag2A7zyMxZhqb//ho8xcmco7y1PYsCuTsBohXNC5EVcmNuestjHB+88/Nws2fwbJX0G95tC0l/OIiPFzHId8dvIl7OwPpf28vIRAVBMn5ub9nedGXTwJzbNEoKr5IjIJmItz+egrqrpORB4ElqnqLLfoGGC62mUqxpy0/IJCFmzeyzvLUpi3YTd5BUr3+Ho8NKoLI3s0o16dID3hm5MBmz6F9R9C0jwoyIVadZ2kgLurqdcCmrlJoWlvaNoTwuud/rpzD8HeTbB3A+zZAHs3wp6NkJny83KhYVAv3tnBtzvPiad+c2e8fnOo2wxC/fP5VYs7i40JNlv2HuKdZSm8vyKFPVm5REeEcWmvZlyZGB+8TT1k74eNn8CGWbBlPhTmQVRT6DwSOo9y/lUfPQxpqyF1BexcCTtXwIFtx5cR3dYnMfSCJt0hrJQ7p48ednf4G3++w8/YfrxMaC2IbQ+xHZ1HgwSo38J5RMRBiP+aeyvrzmJLBMZUEVk5eXy8ehfvLNvBiu0HCQ0RhnaI48rEeIZ2iCOsRhC2IXloD2z8GNbPgq0LQAucnWznUdBpFDTrc+KdbfZ+Nyn4PDLd05kS4uzAm/Z2qmUO7T6+4z+4nWNHF6FhEOPu8OM6QmwniOvk7PhDKseVWJYIjKnCcvIKeOHrZJ7/egtH8gpoFxfJlYnxXNqrGXFRQXivR+Yu2PCRU+2zfRFoITRs4+z8O4+EJj3hdE+GZ6XBzlXOEUPqCuc5Ox1CakJMO3eH3+n4c4NWEFpZLsIsmbU1ZEwVpKrMXbebv3+ynpQDR7i4WxMmDm5Nj/h61fuqH1WnLv/Ifuff+pH9kH3AqWPfNAd2LHbKxXaCwXc7CSCu8+nv/H1FNYYOw5xHUUyH90LtBn6rt/cnSwTGVEJJew7xwEfrWLh5Hx0aRfHmxP4MaOPnq1xOV2EB5GY6O/XcLMjJLLZz930+4DxnpzvDhXklL7NxNzjnXqfaJ7a9/96LCETG+W99fmaJwJhKJCsnj6e/2Myr326jdlgof7ukM9ed0fLUu3lUhfQkp/585wpnPCTUqeIIrQkhNZxHaE13mjte0nwJhaOHju/Yc7PcHX2xnX3RcN7hsmMLqQl1GkKdaKjdEGLaQu1+zrTaDX/5HBHjDJsKZ4nAmEqgsFD5YGUqj3y6kX2HcrmqT3PuHtaBmJNt6VMV0rfAtoXu4xvnBCdAnRioEe782y7Ic+9ULRou5R94qQRqRbmPus5zeH3nRK3vtGPPURBe16laKdq5h0VWbHWOOWWWCIwJsLWpGdz34VpWbD9Ij+b1mTIukR7Ny9kauyrsTz6+09/2DWTtcuZFNoKEQdBqkPPcsHXpO15V56RrUVIoyIPCfOdxbLjAuZSyVpSzE/fjpY/GW5YIjAmQ/YeP8tjcTUxfup3oiDAevaI7V/SOJ6SsO4BVnevei3b8WxdC1k5nXkQcJJx1fMcf3bb8/7hFnKqfkFAgCK9ECnKWCIzxh+3fw5p3oSCXQoUtew6xOjWDngWFXNksiq5N6xK2MwR2lrGMo4dh++Ljd6hGxDo7/oSzIGGwc1mjVbWYU2CJwBivFBY4d7ou+i+kLIGaERytEUlmTh5RhcrQ0BDqRtSgRm4IbC3H8kJqQnwfSLjT+ccf28F2/KZCWCIwpqLlHYFVb8J3z8D+LdAggaxz/skDKb14d/V+mtWvzV8u7sTwro2r9/0ApsqwRGBMRcneD0tegiUvQvY+aNobvWIqM4/25oGPN5F99CC3n9OWW4a0pXZY5Wh2wBiwRGDM6du/1fn3v/INyD8C7S6Egbezq35v/vzBWuZvWkuflg341+XdaRsXGehojfkFSwTGnKrU5fDt005rlxIK3a+GAZPQ2I68tWQH/3x1IfmFyt8u6cy4MxOCtz8AU+lZIjDmZBQWQtLnTgL46RuoVQ8G3A79b4a6Tdiens3kKYtZtCWdAW2ieWR0d1pE1wl01MaUyRKBMSdSWAgHtznX7X/3jNMMcd1mcMHD0HschNeloFB57ZutPDZ3E6Ehwj9Hd2NM3+Z2MthUCZYIjClSWOh0KrJno9u7lPu890en7h+gUVe47EXoOvpYK5RJew7xp/dWs/ynAwztEMs/RnejSb3aAXwjxpwcSwQm+BQWOv3E+vYstXej09tUXvbxclFNnU5GEm9wnht1dXqtcv/l5xcU8uLCZJ6ct5k6YaH85+oeXNqzmR0FmCrH00QgIsOAp3D6LJ6iqo+UUOYq4H6crn5+UNVfdHBvzGk5ehg2f+7U7e9e7+7wfVrGjGridDDSe/zx3qViO0Dt0tv72bArkz++u5o1qRlc1K0xD4zsSmzUSTYQZ0wl4VkiEJFQ4BngfCAFWCois1R1vU+ZdsA9wEBVPSAi1bfBb+NfOZnw41zY8CFsnudU7dRuAI27Q+/rfHqY6uBML6ej+YX83/wknp2fRP06NXnu2t4M79bEwzdijPe8PCLoBySpajKAiEwHRgHrfcpMBJ5R1QMAqrrHw3hMdXfkgNOD1foPYcuXUHAUIhs7O/5OI6HlgNPqP/aHHQf547ur2bQ7i9G9mvHXEZ1pEBFWgW/AmMDwMhE0A3b4jKcA/YuVaQ8gIt/iVB/dr6qfFl+QiNwE3ATQokULT4I1VdThfT6dl3/tNJdcrzn0neh0YRjf97SbS96YlslzX23hox920qhuOK9O6MvQjnbwaqqPQJ8srgG0A4YA8cACEemmqgd9C6nqi8CL4HRe7+8gTSWTlXa88/KfvnXa0W/QCs681dn5N+1dIY2xrdh+gGfnJzFvwx4iwkKZOKg1t57Tlrrh1a/PWhPcvEwEqUBzn/F4d5qvFGCxquYBW0XkR5zEsNTDuExVU1gAe9Y7be9vmOU06YxCTHsY9Hun2qdxtwrZ+asq3yTt49n5W/guOZ36dWpy13ntGT+gJfXrWDWQqZ68TARLgXYi0gonAYwBil8RNBMYC7wqIjE4VUXJHsZkqoLs/ZCyzGm6ecdiSF3h9JULziWcQ+5x/vnHdaywVRYWKp+t382zXyWxOiWDRnVrce/FnRjbrwURtQJ94GyMtzz7hnxRiJ4AACAASURBVKtqvohMAubi1P+/oqrrRORBYJmqznLnXSAi64EC4G5VTfcqJlMJFRbCvk3ODn/HUmfnv+9HZ56EQqMu0GMMNO8PzftBg4QKXX1eQSGzVu3kua+3kLTnEC2j6/DP0d0Y3bsZtWpYC6EmOIhq1apyT0xM1GXLlgU6DHOqcjKcf/s7ljg7/ZTlkJvhzKvd0NnZx/d1dvzNejt95HoRRl4B7yzbwfNfJ5N68AgdG0fx26FtuahrY2qEWl+8pvoRkeWqmljSPDvmNf7x41yY94BT14+ChEBcZ6ephub9nB1/WZ2rV5CsnDze+H47L3+zlX2Hcundoj4PXdqFoR3i7I5gE7QsERhvFeTD/IfhmyecO3aH/tn5x9+sD4TX9VsYGdl5TPkmmamLtpGVk8/g9rH8dkgb+rdqaAnABD1LBMY7WbvhvRth20Kn+Ybh/4Ka/m2M7Wh+IW98/xNPf7mZjCN5DOvSmN8OaUu3+Hp+jcOYyswSgfHGtm/g3Rucph4ufR56jvXr6lWVuet288icDWxLz+astjH8+aJOdG7qv6MQY6oKSwSmYhUWwqKn4IsHnTr/6z5wrvzxox92HOThTzawZNt+2sVF8ur1fRnSPtaqgIwphSUCU3GOHIAPboEf50CXy2Dkf6FWlN9Wn3Igm8fmbuLDVTuJiQzj4cu6cnVic7sKyJgTsERgKsbOlfD2OMjcBcMfg34TPb8CqEhmTh7Pzt/CK99uRYBJQ9ty85A2RNqNYMaUi/1SzOlRhWWvwKeTISIObvgU4ku8VLnC5RUU8taS7Tw5bzP7Dx9ldK9m/OHCDjStb72DGXMyLBGYU5d7CD6+C9a8DW3Pg9EvQZ2Gnq9WVZm3YQ//nLOB5L2HOaN1Q+69uDNdm9mVQMacCksE5tTs3QQzroP0zXDOvXDW70+7uefyWJuawd8/Wc/3yftpHRvBlHGJnNvJbgYz5nRYIjAnb/U78NEdEFbHuSqo9RDPV7nvUC7/mL2BD1am0qBOGA+O6sLYfi2oaSeCjTltlghM+eXnwqf3wLKXocWZcMWrUNf7bhq//nEvv3/7BzKP5HHT4NbcOtT6BDCmIlkiMOWzbzO8P9G5OmjA7XDufRDq7c44N7+Axz7dxJRvttIuLpL/3diPTk3shjBjKpolAlO2o4dhweOw6L9OVdDV06DTCM9Xm7TnELe/tZL1uzK57oyW/OXiToTXtGahjfGCJQJTMlXY+IlzWWjGDugxFs5/ECK97atXVZmxdAcPfLSe8JohvDQukfM7N/J0ncYEO0sE5pf2b4U5f4TNnzlNRV8/B1oO8Hy1B7OPcs/7a5izNo0BbaJ54qqeNK4X7vl6jQl2lgjMcXk58O1TsPDfTv3/BQ9D/994fi4AYHFyOnfOWMXerFwmD+/ITYNaExJil4Qa4w+eJgIRGQY8hdNV5RRVfaTY/AnAYxzv1P7/VHWKlzGZUmyeB7P/AAe2QpfRcOHDULep56vNLyjkqS8288z8JFo0rMN7twygR/P6nq/XGHPcCROBiFwCfKKqhSezYBEJBZ4BzgdSgKUiMktV1xcrOkNVJ53Msk0FykhxzgNs+Aii28J1M6HNUL+sesf+bO6YvpIV2w9yRZ947h/ZxdoHMiYAyvOruxp4UkTew+mAfmM5l90PSFLVZAARmQ6MAoonAhMI+Ufh+2fg60edE8Pn/BUG3AY1avll9R+uSuXeD9YC8PTYXozs4f3RhzGmZCdMBKr6KxGpC4wFpoqIAq8Cb6lqVhkvbQbs8BlPAfqXUO5yERkM/Ajcpao7ihcQkZuAmwBatGhxopDNiWxdAJ/8AfZtgg4Xw7B/QoOWfln1odx87vtwLe+vSKVPywY8eXVPmjes45d1G2NKVq7781U1E3gXmA40AS4DVojIbae5/o+ABFXtDnwOvFbK+l9U1URVTYyNjT3NVQaxrDR490Z47RLIz4Fr3oaxb/otCazacZCLn17IzJWp3HFuO2bcdIYlAWMqgfKcIxgJXA+0BV4H+qnqHhGpg1PN899SXpoKNPcZj+f4SWEAVDXdZ3QK8Gj5QzflduAnp6nopS9DQS6c/Sc46y6/9R+sqry0MJlHP91Eo7rhzPjNmfRN8L6VUmNM+ZTnHMHlwH9UdYHvRFXNFpEby3jdUqCdiLTCSQBjgGt8C4hIE1Xd5Y6OBDaUO3JTtsJCSJ4PS6fApjlOJzEdR8B590N0G7+FcTD7KH945wfmbdjDsC6N+dfl3alXx9oJMqYyKU8iuB8o2lkjIrWBRqq6TVW/KO1FqpovIpOAuTiXj76iqutE5EFgmarOAm53jzjygf3AhFN+J8Zx5CD88BYseQn2b4E6MTDod9Dneqjf/MSvr0Crdhzk1mkr2JOVw98u6cyEAQnWXLQxlZCoatkFRJYBA1T1qDseBnyrqn39EN8vJCYm6rJlywKx6sotbS0sfQlWvw152RDfF/rdBJ1H+e1KoCKqytRF2/jH7A3ERYXzzLW96Wn3BhgTUCKyXFVL7D6wPEcENYqSAICqHnWTgQm0gjzn+v8lL8H2RVAjHLpeAf1+DU17BSSkzJw8/vTuauasTeO8TnE8fmUP6texr4sxlVl5EsFeERnpVuUgIqOAfd6GZcqUuQuWT3Ueh9Kgfks4/yHo9Su/dBVZmrWpGfx22gpSDx7hLxd14teDWllVkDFVQHkSwc3ANBH5P0Bw7g0Y52lU5pdUYft3sORF5yigMN/pJ7jf085zSOCaaFZV3li8nYc+Wk90ZBhv/+YM+rS0q4KMqSrKc0PZFuAMEYl0xw95HpX5ud3rnWYgtn4N4fWg32+g741+vfqnNIdy85n83mo+Xr2LIR1ieeKqnjSMsKogY6qScjXsIiIXA12A8KJDfVV90MO4DED2fpj/D6dryFp1Ydgj0HschEUEOjIANuzK5NZpK9iWfpi7L+zALWe3sRZDjamCynND2fNAHWAozk1fVwBLPI4ruBXkOzeAzX8YcrOg769hyD0Brf/3paq8vWwH9324jnq1a/LmxDM4o3V0oMMyxpyi8hwRDFDV7iKyWlUfEJF/A3O8DixobZnvdBC/dwO0Ots5CmjUOdBRHZN9NJ97P1jL+ytTOattDP+5uiexUf69PNUYU7HKkwhy3OdsEWkKpOO0N2QqUvoW+OyvsOkTaJAAY96EDhc5dwRXEpt3Z/HbaStI2nuIO89rx23ntCPUqoKMqfLKkwg+EpH6OB3IrAAUeMnTqIJJbpbTOfz3z0JomNMExBm/9ftNYCcyb/1ubntrJRG1Qnnjxv4MbBsT6JCMMRWkzEQgIiHAF6p6EHhPRD4GwlU1wy/RVWeFhU5TEF88AId2Q89r4dz7IKpxoCP7hTlrdnHbWyvp3LQuU8YlElfX+hE2pjopMxGoaqGIPAP0csdzgVx/BFat7VjidA6/c6XTFMTYt6BZn0BHVaJZP+zkrhmr6Nm8Pq9e35e64dZgnDHVTXmqhr4QkcuB9/VEDROZsmWkwrz7Yc3bENUERr8E3a6sVOcBfL23PIW73/2BxISGvDKhr3UjaUw1VZ5f9m+A3wH5IpKDc3exqmpdTyOrbla/DR/dAYUFMPhuGHgn1IoMdFSlmrF0O5PfX8OANtG8NC6ROmGWBIyprspzZ3GUPwKp1ha/4FQFtTwLLn3Wbz2Cnar/fbeNv364jrPbx/LCdX0Irxm45iuMMd4rzw1lg0uaXryjGlMCVVjwmHNjWIeL4YpXoGblPtH68jdbeejj9ZzXKY5nru1NrRqWBIyp7spzvH+3z3A40A9YDpzjSUTVRWEhfPYX57LQHmNh5P9BaOWuXnn+6y08Mmcjw7o05umxvQirUa4urY0xVVx5qoYu8R0XkebAk55FVB0U5MNHt8OqadD/ZrjwnxBSuXeqT3+xmSc+/5FLejTliat6UDO0csdrjKk4p/JrTwE6laegiAwTkU0ikiQik8sod7mIqIiU2HtOlZKfC++Md5LAkHucJiIqcRJQVf792Sae+PxHRvdqxpNX97QkYEyQKc85gv/i3E0MTuLoiXOH8YleFwo8A5yPkzyWisgsVV1frFwUcAew+ORCr4RyD8H0a5zmooc9AmfcEuiIyqSqPPLpRl74OpmrE5vzj9HdrMkIY4JQeSqtfTsIzgfeUtVvy/G6fkCSqiYDiMh0YBSwvli5h4B/8fNzEVVP9n6YdqVzk9ilz0PPsYGOqEyqyoMfr+fVb7fxqzNa8ODIrtaEtDFBqjyJ4F0gR1ULwPmnLyJ1VDX7BK9rhtObWZEUoL9vARHpDTRX1U9EpNREICI3ATcBtGjRohwh+1lWGvzvMkhPgqteh04jAh1RmQoLlftmreWN77dz/cAE7hvR2bqUNCaIlacy+Augts94bWDe6a7YbcfoCeD3Jyqrqi+qaqKqJsbGxp7uqivW/q3wyoVwcDtc+26lTwIFhco976/hje+385uzW1sSMMaU64gg3Ld7SlU9JCJ1yvG6VKC5z3i8O61IFNAV+MrdETUGZonISFX1rY6qvHavd44ECnJh3CyIr5ztBRUpKFTufucH3l+Zyu3ntOWu89tbEjDGlOuI4LBbhQOAiPQBjpTjdUuBdiLSSkTCgDHArKKZqpqhqjGqmqCqCcD3QNVJAinL4NXhTjtB18+p9Ekgr6CQO2es4v2Vqfzu/Pb87oIOlgSMMUD5jgjuBN4RkZ047Qw1Bq4+0YtUNV9EJgFzgVDgFVVdJyIPAstUdVbZS6jEkr+Ct66ByFgY96HTkUwltvynA/zlgzVsTMti8vCO3Hx24Du9N8ZUHlKeBkVFpCbQwR3dpKp5nkZVhsTERF22LIAHDRs+gndvgOh2cN37lbL/gCIZ2Xn8a+5G3lqynUZR4TwwqgsXdqm88RpjvCMiy1W1xHu1ynMfwa3ANFVd6443EJGxqvpsBcdZ+a2cBrMmOX0HXPN2pelMvjhV5cNVO/n7J+vZf/goNwxsxV3nt7dmpI0xJSrPnmGiqj5TNKKqB0RkIhBciWDDx/Dhb6H1UBgzDcIiAh1RibbuO8xfZ67lm6R99Iivx9Tr+9G1Wb1Ah2WMqcTKkwhCRUSKOqVx7xgO8zasSqaoFdGY9nDNjErXnzBAbn4Bz3+VzDNfJVErNISHRnXhmv4t7U5hY8wJlScRfArMEJEX3PHfAHO8C6kS2rEYdq2Ci5+olElg0ZZ93PvBWpL3HWZE9ybcN6Kz9StsjCm38iSCP+Hc1XuzO74a58qh4PH9cxBeD3qMCXQkP7PvUC7/+GQD769MpUXDOrx2Qz/Obl/JbrgzxlR65WmGulBEFgNtgKuAGOA9rwOrNDJSnCuFzry10pwXKCxUZizbwSNzNpJ9NJ9JQ9sy6Zy21pOYMeaUlJoIRKQ9MNZ97ANmAKjqUP+EVkkseQlQ6Dcx0JEAsCkti798sIZlPx2gX6uG/OOyrrSNs95EjTGnrqwjgo3AQmCEqiYBiMhdfomqsjh6GJZPhU6XQP3ANnaXk1fAk/M2M2VhMlHhNXj0iu5c2Sfe7g42xpy2shLBaJxmIeaLyKfAdJw7i4PH6hmQcxD6B7ZfgfyCQia9uZJ5G3ZzZZ947rmoEw0jguvCLWOMd0pNBKo6E5gpIhE4/QjcCcSJyHPAB6r6mZ9iDAxVWPwCNOkBLc4IYBjKXz9cx7wNu3lgZBfGD0gIWCzGmOrphI3OqephVX3T7bs4HliJcyVR9ZY8H/ZudI4GAlj98n9fJvHWku3cMqSNJQFjjCdOqnNaVT3g9g1wrlcBVRrfPw8RcdB1dMBCeHvZDv7t9iX8xws7nPgFxhhzCqyX8pLsS4LNcyHxhoDdQDZ/0x7ueX8Ng9rF8Mjl3e2ksDHGM5YISrLkBQip6SSCAFidcpBbp62gY+MonvtVH8Jq2MdkjPGO7WGKy8mAVW9CtysgqpHfV/9T+mFumLqUhhFhvHp9X2sx1BjjOUsExa18A44egv43n7hsBUs/lMv4V5aQX6i8dkM/4qKsvSBjjPcsEfgqLHAuGW1xJjTt6ddVZx/N54bXlrErI4eXx/elTWykX9dvjAlelgh8/fgpHPzJ70cDRTeMrUk5yH/H9qJPywZ+Xb8xJrh5mghEZJiIbBKRJBGZXML8m0VkjYisEpFvRKSzl/Gc0PfPQb3m0HGE31apqtw7cy1fbtzDg6O6coF1JWmM8TPPEoHbgc0zwHCgMzC2hB39m6raTVV7Ao8CT3gVzwmlrYVtC6HvryHUfydon/piM9OX7mDS0Lb86oyWfluvMcYU8fKIoB+QpKrJqnoUp62iUb4FVDXTZzQCUA/jKdvi56BGbeg9zm+rnL5kO0/O28zlveP5/QXt/bZeY4zx5eVf32bADp/xFKB/8UIicivwO5zuL88paUEichNO5zi0aOFBK6CH98Hqd6DXtX7rkP7Ljbv5y8y1DG4fyyOXd7MbxowxARPwk8Wq+oyqtsFpv+jeUsq8qKqJqpoYG+tBD1zLX4WCXL+dJF614yC3TltJ5yZ1ee7a3tQMDfjHYIwJYl7ugVKB5j7j8e600kwHLvUwnpLlH4UlU6DNORDrfXs+2/Y5N4zFRIXxyoS+RNgNY8aYAPMyESwF2olIKxEJw+nbYJZvARFp5zN6MbDZw3hKtv5DOJTmlz4H9mblMu6VJQC8fkN/YqMC046RMcb48uzvqKrmi8gkYC4QCryiqutE5EFgmarOAiaJyHlAHnAAGO9VPKVa/BxEt4W253m6msO5+dz42lL2ZOXw1sQzaBVTOfo/NsYYT+slVHU2MLvYtPt8hu/wcv0ntGMppC6H4Y9BiLf19Pd9uI61qRm8NC6RXi3shjFjTOUR3GcpFz8HtepCz7GerubTtbt4b0UKk4a25dxO/m/IzhhjyhK8iSAj1Tk/0Hsc1IrybDV7snK45/01dGtWj9vObXfiFxhjjJ8FbyJYOgW0EPpN9GwVqsrk99aQfbSA/1zdwy4TNcZUSsG5Z8o7AsunQoeLoEGCZ6uZvnQHX27cw+ThHWkb591RhzHGnI7gTASr34Yj+z29geyn9MM89PF6BraNZvyZCZ6txxhjTlfwJQJVWPw8NOoKCWd5sor8gkLumrGKGiHC41f2ICTEmo8wxlRewZcIti6APeudowGP2vd5YUEyK7Yf5KFLu9KkXm1P1mGMMRUl+BLB989BnWjodqUni1+bmsF/Pv+Ri7s3YWSPpp6swxhjKlJwJYL9yU4vZIk3QM2K7w84J6+Au2asomFEGA9f2tVaFDXGVAnB1eLZ4hchJBQSb/Rk8Y/N3cTmPYd47YZ+1K8T5sk6jDGmogXPEUFOJqx8A7pcBnWbVPjiFyXt4+VvtjLuzJac3d6DprKNMcYjwZMIVr0JR7M8aWU040gef3jnB1rHRHDP8E4VvnxjjPFS8FQNJQyEsydDfJ8KX/QDs9axOyuX924ZQO2w0ApfvjHGeCl4EkHjbs6jgs1es4v3V6Zyx7nt6Nm8foUv3xhjvBY8VUMe2JOZw58/WEP3+HpMOqdtoMMxxphTYongFKkqf3xvNTl5Bfzn6p7WoJwxpsqyvdcpmrZ4O19t2ss9wzvRJjYy0OEYY8wp8zQRiMgwEdkkIkkiMrmE+b8TkfUislpEvhCRll7GU1G27jvMw59sYFC7GK47o0qEbIwxpfIsEYhIKPAMMBzoDIwVkc7Fiq0EElW1O/Au8KhX8VSUogblwmqE8NgV1qCcMabq8/KIoB+QpKrJqnoUmA6M8i2gqvNVNdsd/R6I9zCeCvHcV1tYtcNpUK5xvYpvpsIYY/zNy0TQDNjhM57iTivNjcCckmaIyE0iskxElu3du7cCQzw5a1IyeOqLzYzs0dQalDPGVBuV4mSxiPwKSAQeK2m+qr6oqomqmhgbG5jmG3LyCrhzxkpiImvx0KiuAYnBGGO84OUNZalAc5/xeHfaz4jIecBfgLNVNdfDeE7LSwuS2bL3MP+7sR/16tQMdDjGGFNhvDwiWAq0E5FWIhIGjAFm+RYQkV7AC8BIVd3jYSynJSevgKmLtjG0QyyD2lmDcsaY6sWzRKCq+cAkYC6wAXhbVdeJyIMiMtIt9hgQCbwjIqtEZFYpiwuoD1amkn74KBMHtw50KMYYU+E8bWtIVWcDs4tNu89n+Dwv118RCguVlxYm07VZXc5sHR3ocIwxpsJVipPFldmXG/eQvPcwEwe1th7HjDHVkiWCE3hxYTLN6tfmom4V35mNMcZUBpYIyrBqx0GWbN3P9QMTrFE5Y0y1ZXu3Mry0MJmo8BqM6dci0KEYY4xnLBGUYsf+bOas2cU1/VsQWSt4+u8xxgQfSwSlePmbrYSIcP2AVoEOxRhjPGWJoAQHs4/y9rIdjOzZ1BqWM8ZUe5YISjBt8XayjxYwcZDdQGaMqf4sERSTm+80JzGoXQydmtQNdDjGGOM5SwTFfLhqJ3uzcrnJmpMwxgQJSwQ+VJWXFiTTsXEUZ7WNCXQ4xhjjF5YIfHz141427znETYOtOQljTPCwRODjpQXJNK4bzoju1vuYMSZ4WCJwrU3NYNGWdK4fmEBYDdssxpjgYXs815SFyUTWqsHY/tachDEmuFgiAHYePMJHq3dxdd/m1A23biiNMcHFEgHw6rdbAbh+YEJgAzHGmADwNBGIyDAR2SQiSSIyuYT5g0VkhYjki8gVXsZSmsycPN5asoOLuzUhvkGdQIRgjDEB5VkiEJFQ4BlgONAZGCsinYsV2w5MAN70Ko4Tmb5kO4dy8605CWNM0PKyfeV+QJKqJgOIyHRgFLC+qICqbnPnFXoYR6mO5hfyyjfbOLN1NN3i6wUiBGOMCTgvq4aaATt8xlPcaSdNRG4SkWUismzv3r0VEhzAJ2t2kpaZY81JGGOCWpU4WayqL6pqoqomxsbGVtQyeXHBVtrFRXJ2+4pZpjHGVEVeJoJUoLnPeLw7rVL4NimdDbsymTioNSEh1pyEMSZ4eZkIlgLtRKSViIQBY4BZHq7vpLy4MJmYyFqM6mXNSRhjgptniUBV84FJwFxgA/C2qq4TkQdFZCSAiPQVkRTgSuAFEVnnVTy+NuzKZMGPe7l+YAK1aoT6Y5XGGFNpedoru6rOBmYXm3afz/BSnCojv5qycCu1a4ZyrTUnYYwxVeNkcUVKy8hh1g+pXN23OfXrhAU6HGOMCbigSwRTF22joFC5YWCrQIdijDGVQlAlgkO5+Uxb/BPDuzahRbQ1J2GMMRBkiWDG0h1k5eTz60F2NGCMMUWCJhHkFxTyyjdb6ZfQkF4tGgQ6HGOMqTSCJhHMXptG6sEjTLTmJIwx5meCJhFEhIVyfudGnNsxLtChGGNMpeLpfQSVybmdGnFup0aBDsMYYyqdoDkiMMYYUzJLBMYYE+QsERhjTJCzRGCMMUHOEoExxgQ5SwTGGBPkLBEYY0yQs0RgjDFBTlQ10DGcFBHZC/x0ii+PAfZVYDgVzeI7PRbf6avsMVp8p66lqsaWNKPKJYLTISLLVDUx0HGUxuI7PRbf6avsMVp83rCqIWOMCXKWCIwxJsgFWyJ4MdABnIDFd3osvtNX2WO0+DwQVOcIjDHG/FKwHREYY4wpxhKBMcYEuWqZCERkmIhsEpEkEZlcwvxaIjLDnb9YRBL8GFtzEZkvIutFZJ2I3FFCmSEikiEiq9zHff6Kz13/NhFZ4657WQnzRUSedrffahHp7cfYOvhsl1UikikidxYr4/ftJyKviMgeEVnrM62hiHwuIpvd5xI7yxaR8W6ZzSIy3k+xPSYiG93P7wMRqV/Ka8v8Lngc4/0ikurzOV5UymvL/L17GN8Mn9i2iciqUl7rl214WlS1Wj2AUGAL0BoIA34AOhcr81vgeXd4DDDDj/E1AXq7w1HAjyXENwT4OIDbcBsQU8b8i4A5gABnAIsD+Fmn4dwoE9DtBwwGegNrfaY9Ckx2hycD/yrhdQ2BZPe5gTvcwA+xXQDUcIf/VVJs5fkueBzj/cAfyvEdKPP37lV8xeb/G7gvkNvwdB7V8YigH5CkqsmqehSYDowqVmYU8Jo7/C5wroiIP4JT1V2qusIdzgI2AM38se4KNAp4XR3fA/VFpEkA4jgX2KKqp3qneYVR1QXA/mKTfb9nrwGXlvDSC4HPVXW/qh4APgeGeR2bqn6mqvnu6PdAfEWu82SVsv3Kozy/99NWVnzuvuMq4K2KXq+/VMdE0AzY4TOewi93tMfKuD+GDCDaL9H5cKukegGLS5h9poj8ICJzRKSLXwMDBT4TkeUiclMJ88uzjf1hDKX/+AK5/Yo0UtVd7nAaUFKn2ZVhW96Ac4RXkhN9F7w2ya2+eqWUqrXKsP0GAbtVdXMp8wO9DU+oOiaCKkFEIoH3gDtVNbPY7BU41R09gP8CM/0c3lmq2hsYDtwqIoP9vP4TEpEwYCTwTgmzA739fkGdOoJKd622iPwFyAemlVIkkN+F54A2QE9gF071S2U0lrKPBir976k6JoJUoLnPeLw7rcQyIlIDqAek+yU6Z501cZLANFV9v/h8Vc1U1UPu8GygpojE+Cs+VU11n/cAH+Acfvsqzzb22nBgharuLj4j0NvPx+6iKjP3eU8JZQK2LUVkAjACuNZNVL9Qju+CZ1R1t6oWqGoh8FIp6w7od9Hdf4wGZpRWJpDbsLyqYyJYCrQTkVbuv8YxwKxiZWYBRVdnXAF8WdoPoaK59YkvAxtU9YlSyjQuOmchIv1wPie/JCoRiRCRqKJhnJOKa4sVmwWMc68eOgPI8KkC8ZdS/4UFcvsV4/s9Gw98WEKZucAFItLArfq4wJ3mKREZBvwRGKmq2aWUKc93wcsYfc87XVbKusvze/fSecBGVU0paWagt2G5BfpstRcPnKtafsS5muAv7rQHcb70AOE4VQpJwBKgtR9jOwunimA1sMp9XATcDNzslpkErMO5AuJ7YIAf42vtrvcHN4ai7ecbnwDPuNt3DZDo5883ztgI3AAAAktJREFUAmfHXs9nWkC3H05S2gXk4dRT34hz3ukLYDMwD2jolk0Epvi89gb3u5gEXO+n2JJw6taLvoNFV9E1BWaX9V3w4/b7n/v9Wo2zc29SPEZ3/Be/d3/E506fWvS98ykbkG14Og9rYsIYY4JcdawaMsYYcxIsERhjTJCzRGCMMUHOEoExxgQ5SwTGGBPkLBEYU4yIFBRr4bTCWrQUkQTfFiyNqQxqBDoAYyqhI6raM9BBGOMvdkRgTDm57cr/f3t37FJVGMZx/PtDGi4EEgUuEQ46RQ3R5Oi/0CDSFE0O4hT9A01NYbXoEP4XIhiIkOCWf0C0Jehg0CISP4f3ES63a3RBu8L5feBw3/NcOJx3et73vOc879uqLb8vaabi05I+V3G0bUkPKj5Vtf6/1jFXl5qQtK62H8WWpN7YOhVBEkHEML2BR0MLff/9tP0I+AC8q9h7YMP2Y1rxttWKrwI7bsXvntC+LAWYBT7afgicAM+uuT8Rf5UviyMGSPpl+/aQ+Hdg3va3Khx4aPuupGNa+YOziv+wfU/SEXDf9mnfNaZp+w/M1vlr4JbtN9ffs4jhMiOIGI0vaY/itK/9m6zVxZglEUSMZqHvd6/aX2hVLwGeA7vV3gaWACRNSJr8XzcZMYqMRCL+1BvYiHzT9sUrpHckHdBG9YsVWwY+SXoFHAEvKr4CrEl6SRv5L9EqWEbcKFkjiPhHtUbw1PbxuO8l4irl0VBERMdlRhAR0XGZEUREdFwSQURExyURRER0XBJBRETHJRFERHTcOXm5ADAJh5tMAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Training Accuracy vs Validation Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Bwv7r0ZocOq6",
        "outputId": "23cfe72c-2812-4759-a643-5cc6b41ab5dc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Training Loss vs Validation Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sP1ZRVsoV4Au",
        "outputId": "ed966b84-5a43-4a52-8065-f984d283cf55"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 32, 32)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "te = cv2.imread(\"/content/drive/MyDrive/Datasets/falade test/Abà/Abà.jpg\",0)\n",
        "te = cv2.resize(te,(32,32))\n",
        "thresh = cv2.threshold(te, 0, 255,cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]\n",
        "dilated = cv2.dilate(thresh, None, iterations=2)\n",
        "blurred = cv2.GaussianBlur(dilated, (5, 5), 0)\n",
        "input_image = np.expand_dims(blurred, axis=0)\n",
        "input_image.shape\n",
        "#te = te.reshape(-1,32,32,3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "irr5c4HyVuOH"
      },
      "outputs": [],
      "source": [
        "predic = model.predict(input_image)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AcftI25eXmwR",
        "outputId": "49fff569-a9f6-4658-87fe-4cd52d440722"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['N'], dtype='<U3')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "ypr = LB.inverse_transform(predic)\n",
        "ypr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xPnla3QTcS9V"
      },
      "outputs": [],
      "source": [
        "def sort_contours(cnts, method=\"left-to-right\"):\n",
        "    reverse = False\n",
        "    i = 0\n",
        "    if method == \"right-to-left\" or method == \"bottom-to-top\":\n",
        "        reverse = True\n",
        "    if method == \"top-to-bottom\" or method == \"bottom-to-top\":\n",
        "        i = 1\n",
        "    boundingBoxes = [cv2.boundingRect(c) for c in cnts]\n",
        "    (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),\n",
        "    key=lambda b:b[1][i], reverse=reverse))\n",
        "    # return the list of sorted contours and bounding boxes\n",
        "    return (cnts, boundingBoxes)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9SGuKdWEcaBO"
      },
      "outputs": [],
      "source": [
        "def get_letters(img):\n",
        "    letters = []\n",
        "    image = cv2.imread(img)\n",
        "    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
        "    thresh = cv2.threshold(gray, 0, 255,cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]\n",
        "    blurred = cv2.GaussianBlur(thresh, (5, 5), 0)\n",
        "    cnts = cv2.findContours(blurred.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)\n",
        "    cnts = imutils.grab_contours(cnts)\n",
        "    cnts = sort_contours(cnts, method=\"left-to-right\")[0]\n",
        "    # loop over the contours\n",
        "    for c in cnts:\n",
        "        if cv2.contourArea(c) > 10:\n",
        "            (x, y, w, h) = cv2.boundingRect(c)\n",
        "            cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)\n",
        "        roi = gray[y:y + h, x:x + w]\n",
        "        thresh = cv2.threshold(roi, 0, 255,cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]\n",
        "        thresh = cv2.resize(thresh, (32, 32), interpolation = cv2.INTER_CUBIC)\n",
        "        thresh = thresh.astype(\"float32\") / 255.0\n",
        "        thresh = np.expand_dims(thresh, axis=-1)\n",
        "        thresh = thresh.reshape(1,32,32,1)\n",
        "        ypred = model.predict(thresh)\n",
        "        ypred = LB.inverse_transform(ypred)\n",
        "        [x] = ypred\n",
        "        letters.append(x)\n",
        "    return letters, image\n",
        "\n",
        "#plt.imshow(image)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U8COUvpucgAk"
      },
      "outputs": [],
      "source": [
        "def get_word(letter):\n",
        "    words = \"\".join(letter)\n",
        "    return words"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "id": "p4fYKqqOclQT",
        "outputId": "3ddb429a-c46d-4bf6-859d-f90654df029c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ÚhUn\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f16fc717250>"
            ]
          },
          "metadata": {},
          "execution_count": 89
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "letter,image = get_letters(\"/content/drive/MyDrive/Datasets/falade train/Ohun/Ohun4.jpg\")\n",
        "word = get_word(letter)\n",
        "print(word)\n",
        "plt.imshow(image)\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "provenance": [],
      "mount_file_id": "1RZg3VyXctrLZFnzTiAJCIHYJvHFFfDr9",
      "authorship_tag": "ABX9TyND3V40dBUmaUl/zmOi8kpN",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}