Handwitten_Character_Recognition.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### Reading and Loading the Dataset"
      ],
      "metadata": {
        "id": "G1cu_fRubPmn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KRxYJIIs9s7d",
        "outputId": "6eee18c0-d994-40dc-e5f1-5186e0df599f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: imutils in /usr/local/lib/python3.7/dist-packages (0.5.4)\n"
          ]
        }
      ],
      "source": [
        "!pip install imutils"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          }
        },
        "id": "LN1CdkMP9u13",
        "outputId": "670c8e52-6f33-495c-a058-4d73ed2aa04c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-480cc968-adc3-4640-ab5d-9a6a8ca48fd3\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-480cc968-adc3-4640-ab5d-9a6a8ca48fd3\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"fatimabendaouch\",\"key\":\"6d657a529bbc7b0e516b3f93d53e8959\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JhosMH_H-AfV"
      },
      "outputs": [],
      "source": [
        "!pip install -q kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wsxfhGi--IME"
      },
      "outputs": [],
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7Anoxj4F-JXu"
      },
      "outputs": [],
      "source": [
        "!chmod 600 ~/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iwwxuwYc-Na6",
        "outputId": "e7a6fa0b-4652-46df-cd86-290c2903dd78"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading handwritten-characters.zip to /content\n",
            "100% 1.62G/1.62G [00:11<00:00, 186MB/s]\n",
            "100% 1.62G/1.62G [00:11<00:00, 154MB/s]\n"
          ]
        }
      ],
      "source": [
        "!kaggle datasets download -d vaibhao/handwritten-characters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bvDWElBh-mSX",
        "outputId": "d199a292-96b4-460e-c92e-abb062375041"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading handwriting-recognition.zip to /content\n",
            "100% 1.26G/1.26G [00:25<00:00, 62.0MB/s]\n",
            "100% 1.26G/1.26G [00:25<00:00, 53.9MB/s]\n"
          ]
        }
      ],
      "source": [
        "!kaggle datasets download -d landlord/handwriting-recognition"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ts66pFla-qCW",
        "outputId": "b4eae4e2-374a-46de-98a8-53b720871fb0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mLe flux de sortie a été tronqué et ne contient que les 5000 dernières lignes.\u001b[0m\n",
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            "  inflating: dataset/Validation/Z/99.jpg  \n"
          ]
        }
      ],
      "source": [
        "!unzip /content/handwritten-characters.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v57MPNYq-xiH",
        "outputId": "0e19deaf-3bfe-42b3-9753-690b6838d9b8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mLe flux de sortie a été tronqué et ne contient que les 5000 dernières lignes.\u001b[0m\n",
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            "  inflating: validation_v2/validation/VALIDATION_9998.jpg  \n",
            "  inflating: validation_v2/validation/VALIDATION_9999.jpg  \n",
            "  inflating: written_name_test_v2.csv  \n",
            "  inflating: written_name_train_v2.csv  \n",
            "  inflating: written_name_validation_v2.csv  \n"
          ]
        }
      ],
      "source": [
        "!unzip /content/handwriting-recognition.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Importing the necessary dependencies "
      ],
      "metadata": {
        "id": "JGXD4rC-bktE"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q6SZfxMU-4Rn"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "import os\n",
        "import random \n",
        "import cv2\n",
        "import imutils\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\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 sklearn.preprocessing import LabelBinarizer\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"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Upload Validation and Training Data "
      ],
      "metadata": {
        "id": "cpw2BKu4c8jJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n9X5ZTAM-978"
      },
      "outputs": [],
      "source": [
        "dir = \"/content/Train\"\n",
        "dir = \"/content/Train\"\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",
        "        train_data.append([img,i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fPTAjVWu_CYd",
        "outputId": "d3110f90-f1ad-4cfa-b638-03aae91ee2dd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "140000"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "len(train_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GpJLK1aM_Hrp"
      },
      "outputs": [],
      "source": [
        "val_dir = \"/content/Validation\"\n",
        "val_data = []\n",
        "img_size = 32\n",
        "for i in os.listdir(val_dir):\n",
        "    if i in non_chars:\n",
        "        continue\n",
        "    count = 0\n",
        "    sub_directory = os.path.join(val_dir,i)\n",
        "    for j in os.listdir(sub_directory):\n",
        "        count+=1\n",
        "        if count > 1000:\n",
        "            break\n",
        "        img = cv2.imread(os.path.join(sub_directory,j),0)\n",
        "        img = cv2.resize(img,(img_size,img_size))\n",
        "        val_data.append([img,i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9J2kAYKo_Ll-",
        "outputId": "5b4e9518-0b89-4dd5-a80f-18db713b4027"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15209"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "len(val_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SPj6EuzJ_PqO"
      },
      "outputs": [],
      "source": [
        "random.shuffle(train_data)\n",
        "random.shuffle(val_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7Qwwv06J_TFc"
      },
      "outputs": [],
      "source": [
        "train_X = []\n",
        "train_Y = []\n",
        "for features,label in train_data:\n",
        "    train_X.append(features)\n",
        "    train_Y.append(label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sErsNWin_XAD"
      },
      "outputs": [],
      "source": [
        "val_X = []\n",
        "val_Y = []\n",
        "for features,label in val_data:\n",
        "    val_X.append(features)\n",
        "    val_Y.append(label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qNHBE8NQ_aK-"
      },
      "outputs": [],
      "source": [
        "LB = LabelBinarizer()\n",
        "train_Y = LB.fit_transform(train_Y)\n",
        "val_Y = LB.fit_transform(val_Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iJcZmUiR_eJt"
      },
      "outputs": [],
      "source": [
        "train_X = np.array(train_X)/255.0\n",
        "train_X = train_X.reshape(-1,32,32,1)\n",
        "train_Y = np.array(train_Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kz9RFDxU_htW"
      },
      "outputs": [],
      "source": [
        "val_X = np.array(val_X)/255.0\n",
        "val_X = val_X.reshape(-1,32,32,1)\n",
        "val_Y = np.array(val_Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8SMRx4kr_qvS",
        "outputId": "4abeef77-62ad-45c7-b01c-edc479169f0b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(140000, 32, 32, 1) (15209, 32, 32, 1)\n"
          ]
        }
      ],
      "source": [
        "print(train_X.shape,val_X.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z8A0kfCb_txm",
        "outputId": "f2607344-154c-47ca-cc40-bebc1488faf5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(140000, 35) (15209, 35)\n"
          ]
        }
      ],
      "source": [
        "print(train_Y.shape,val_Y.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3zScurI_36-"
      },
      "source": [
        "### **Random Forest**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cIz-TLNq_yYp"
      },
      "outputs": [],
      "source": [
        "X_train = train_X.reshape(140000,1*32*32)\n",
        "X_val = val_X.reshape(15209,1*32*32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EIKYTPkJAQhr"
      },
      "outputs": [],
      "source": [
        "from sklearn.decomposition import PCA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "feO9M7FzATR9"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TNJCU51jAWI6"
      },
      "outputs": [],
      "source": [
        "clf= RandomForestClassifier()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "PQ5Jb0ggAZZt",
        "outputId": "daddc68f-5f51-40c7-9751-04f5fa4095d3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ERROR:root:Internal Python error in the inspect module.\n",
            "Below is the traceback from this internal error.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2882, in run_code\n",
            "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
            "  File \"<ipython-input-27-ec874cfcbac2>\", line 1, in <module>\n",
            "    clf.fit(X_train, train_Y)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py\", line 459, in fit\n",
            "    for i, t in enumerate(trees)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 1046, in __call__\n",
            "    while self.dispatch_one_batch(iterator):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 861, in dispatch_one_batch\n",
            "    self._dispatch(tasks)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 779, in _dispatch\n",
            "    job = self._backend.apply_async(batch, callback=cb)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
            "    result = ImmediateResult(func)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
            "    self.results = batch()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 263, in __call__\n",
            "    for func, args, kwargs in self.items]\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 263, in <listcomp>\n",
            "    for func, args, kwargs in self.items]\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/utils/fixes.py\", line 211, in __call__\n",
            "    return self.function(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py\", line 185, in _parallel_build_trees\n",
            "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/tree/_classes.py\", line 942, in fit\n",
            "    X_idx_sorted=X_idx_sorted,\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/tree/_classes.py\", line 420, in fit\n",
            "    builder.build(self.tree_, X, y, sample_weight)\n",
            "KeyboardInterrupt\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 1823, in showtraceback\n",
            "    stb = value._render_traceback_()\n",
            "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/ultratb.py\", line 1132, in get_records\n",
            "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/ultratb.py\", line 313, in wrapped\n",
            "    return f(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/ultratb.py\", line 358, in _fixed_getinnerframes\n",
            "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
            "  File \"/usr/lib/python3.7/inspect.py\", line 1502, in getinnerframes\n",
            "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
            "  File \"/usr/lib/python3.7/inspect.py\", line 1464, in getframeinfo\n",
            "    lines, lnum = findsource(frame)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/ultratb.py\", line 182, in findsource\n",
            "    lines = linecache.getlines(file, globals_dict)\n",
            "  File \"/usr/lib/python3.7/linecache.py\", line 47, in getlines\n",
            "    return updatecache(filename, module_globals)\n",
            "  File \"/usr/lib/python3.7/linecache.py\", line 137, in updatecache\n",
            "    lines = fp.readlines()\n",
            "  File \"/usr/lib/python3.7/codecs.py\", line 319, in decode\n",
            "    def decode(self, input, final=False):\n",
            "KeyboardInterrupt\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m"
          ]
        }
      ],
      "source": [
        "clf.fit(X_train, train_Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "foy0PVDZAdTz"
      },
      "outputs": [],
      "source": [
        "y_pred = clf.predict(X_val)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TTEpON-vApNg"
      },
      "source": [
        "## **Evaluation:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z0qSuARPAhpG",
        "outputId": "b00475a0-c547-4def-c532-43e5401edbd1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Precison :  0.9449059516693733\n",
            "Rappel :  0.6141038012367558\n",
            "f-score de Modèle est:  0.7324387311496283\n",
            "Taux de réussite de modèle est : 0.6354132421592478\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "Precison =precision_score(val_Y, y_pred, average='macro')\n",
        "print(\"Precison : \",Precison)\n",
        "\n",
        "Rappel =recall_score(val_Y, y_pred, average='macro')\n",
        "print(\"Rappel : \",Rappel)\n",
        "\n",
        "f1_score= f1_score(val_Y, y_pred , average='macro')\n",
        "print(\"f-score de Modèle est: \",f1_score)\n",
        "\n",
        "accuracy = accuracy_score(val_Y, y_pred)\n",
        "print(\"Taux de réussite de modèle est :\",accuracy)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RmnEzY6tBGOZ"
      },
      "source": [
        "### **KNN**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AorDamubBNgk"
      },
      "outputs": [],
      "source": [
        "clf2 = KNeighborsClassifier(n_neighbors=7)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u96lzdbDBTt0"
      },
      "outputs": [],
      "source": [
        "clf2 = clf2.fit(X_train, train_Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CYQY4ittBfKy"
      },
      "outputs": [],
      "source": [
        "y_pred = clf2.predict(X_val)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7iKXo49-BiGk",
        "outputId": "db20841f-f7ff-4103-c1eb-be55f1bb7a9b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(15209, 35)"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qjQm63SEB7gZ",
        "outputId": "55beaf00-d873-456e-d9e7-b446280c3aeb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(15209, 35)"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "val_Y.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_g6-HnHcB-_8",
        "outputId": "92eaa1ae-3ec1-449f-a898-fc705652f976"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: mlxtend in /usr/local/lib/python3.7/dist-packages (0.14.0)\n",
            "Requirement already satisfied: pandas>=0.17.1 in /usr/local/lib/python3.7/dist-packages (from mlxtend) (1.1.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from mlxtend) (57.4.0)\n",
            "Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.7/dist-packages (from mlxtend) (1.19.5)\n",
            "Requirement already satisfied: matplotlib>=1.5.1 in /usr/local/lib/python3.7/dist-packages (from mlxtend) (3.2.2)\n",
            "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.7/dist-packages (from mlxtend) (1.0.1)\n",
            "Requirement already satisfied: scipy>=0.17 in /usr/local/lib/python3.7/dist-packages (from mlxtend) (1.4.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.5.1->mlxtend) (3.0.6)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.5.1->mlxtend) (1.3.2)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.5.1->mlxtend) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.5.1->mlxtend) (0.11.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.17.1->mlxtend) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=1.5.1->mlxtend) (1.15.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.18->mlxtend) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.18->mlxtend) (3.0.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install mlxtend"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DChaf62SCDHB",
        "outputId": "2355939b-1a85-4bfb-d3ca-f3462234b49d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 2520x2520 with 0 Axes>"
            ]
          },
          "metadata": {},
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            "text/plain": [
              "<Figure size 2520x2520 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay,classification_report\n",
        "import matplotlib.pyplot as plt\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "\n",
        "#disp.plot()\n",
        "#print('calssification report')\n",
        "#print(classification_report(val_Y, y_pred, target_names=clf2.classes_))\n",
        "plt.figure(figsize = (35,35))\n",
        "cm  = confusion_matrix(val_Y.argmax(axis=1) , y_pred.argmax(axis=1))\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=clf2.classes_)\n",
        "fig,ax=plot_confusion_matrix(cm,figsize = (35,35))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fUr8oejOCVKH"
      },
      "source": [
        "### **Test**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E3rvjLDJCHW8"
      },
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KdJ5wEY2CcPR"
      },
      "outputs": [],
      "source": [
        "def get_letters(img):\n",
        "    letters = []\n",
        "    image = cv2.imread(img)\n",
        "    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
        "    ret,thresh1 = cv2.threshold(gray ,127,255,cv2.THRESH_BINARY_INV)\n",
        "    dilated = cv2.dilate(thresh1, None, iterations=2)\n",
        "\n",
        "    cnts = cv2.findContours(dilated.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 = clf2.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": "sjxTsGCUCiYu"
      },
      "outputs": [],
      "source": [
        "def get_word(letter):\n",
        "    word = \"\".join(letter)\n",
        "    return word"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "id": "NGfoebkZCmD7",
        "outputId": "06f85ee3-6d70-4251-97d5-d6f4c9d003af"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "BBNF0\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f1e14609d50>"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAABQCAYAAAAAwi69AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAT3klEQVR4nO3dfXRU9ZnA8e9z54VMXkgI4SVGCIQiAi0CoRJXUItClaIUkR4tZ9eqq+3u1lpPtaWraNtzLGd1dXva00rlCCi60lXabo2KBsS2m3ZRKCgK4UVAEAOJBPKGYZg7v/3j3plMJm8TyNtln4+Hw8zv3rn3+c0Pn7n3ub87I8YYlFJKeY/V1wEopZQ6O5rAlVLKozSBK6WUR2kCV0opj9IErpRSHqUJXCmlPOqcEriIXCsiu0Vkn4gs6a6glFJKdU7Odh64iPiAPcBs4GPgHeAWY8zO7gtPKaVUe87lCPxSYJ8xZr8xJgysBeZ3T1hKKaU64z+H1xYAhxOefwxM7+gFeXl5ZtSoUeewS6WU+v9n69atnxpjhiS3n0sCT4mI3AXcBTBy5Ei2bNnS07tUSqnzioh81Fb7uZRQjgAjEp5f6La1YIx5yhgzzRgzbciQVh8gSimlztK5JPB3gLEiMlpEgsDNwB+6JyyllFKdOesSijEmIiLfBl4HfMBKY8wH3RaZUkqpDp1TDdwY8yrwajfFopRSqgv0TkyllPIoTeBKKeVRmsCVUsqjNIErpZRHaQJXSimP0gSulFIepQlcKaU8ShO4Ukp5VI9/mdXZKKecT/n0rF4bIMBc5nZbLLvYxR72pLy+hcX1XN9t++/IKU5RRlmXXzeb2aST3gMRKaV6U79L4NvYxt3czTa2ndXrs8iilNIuvWYa09pMaPvYx0/5Kc/xXMrbChJkAxuYycwuxdBVpzjFy7zMzdzc5deuZS3Xc70mcaU87qx/kedsTJs2zXT2dbIllLCZzb0UkeN3/I45Zg4hQvE2EeEe7uHn/LzL2/Pj5wxn2l0ejUYRkQ630dFyYwwVVDBBJnQ5tpid7GQ848/69Uqp3iMiW40x05Lb+9URePzDxM1doboQdq1NOBzG7/eTlpZGRkYGmZmZzrrGSXQGQ4QIh+TQWe13AQt4h3eYaqZijGmZPN2HA81A8shLaHb2a9s2NTU1pGWkUZ1RndL+bNvG5/MhIkSjUQAsy4r3v7PkDu575a5mYTHajMZgaGhooKqqiuHDh5ORngHirCsIBzhAVKIpxaiU6v/6VQK3bRvjaz4juPQ3l3JqxSn2799Pfn4+kyZNYs6cOdx4442EQiFs2yYYDBKJRPjI+oixvrEtN2hgwMkB2LZNIBDAGEMkEsEYQ25uLset4y0SmojEk6oxBmM1x/J183V+yS+dzRqDZVnYts2xY8dYsmQJM2+cyTcXfLPD/h3lKAYDATe8qHGe4yRhO2pjWVY8gWeSSRZZbW4raprjHshA9rKX+vp61qxZw4MPPsjK/1zJtddeC6b5iH+wDOYkJzsZBaWUV/SrBJ585FlVVcVXr/kq48aNw7Zt/vrXv/Loo4+ya9culi5dSnq6U8NNPHptIQpz75jLJ598wvTp0zl58iQVFRU0NjZSVlbG5GGTqZIqZ98Ixph48vb5fAjN8YglEHWSoWVZ8fWqq6t58803ueSaSzrsWy21jGIUpznd3Jg8ByhpNO7nfn7Mj1uUdmJ8lq/5iYEzZ87w3HPP8dBDD/HQQw8xY8aM+HsS+7DB12ozSikP61cJPDlpXnLJJXztwq8xadIkAL7yla/w61//mo0bN1JeXs6sWbMwxuD3+/G1k512797NokWLWLRoEeFwmIaGBoLBIFlZWSTsKp7sfD5nO7ZtO0fnsc3Gqjvuh0wsgdfV1REIBLjssss67NtgBmNjd+n9eIzHCBPmZ/ys1TLbtuOjZ4yhsrKSX/3qVzz55JMsXLgQy7Liy6LRqNOvziszSikP6VcJPLkGfmD/AY4FjmHbNiJCXl4eV111Fbt27aKsrIxrrrkmXkNOrAnHWGLx8MMPM2PGDIYPH+5s2k3AsbpwnLQ8CrcsC0uaD5HD4TBNdhOhUCj+oXH8+HHKy8sZP348Y8aMSbmflrHicbdaZlkYaS6ttCf2QQNwOnyaZ599lquvvjqevCORCH6/vzl5g/MhpElcqfNGv7qRJxqNtkhcx2uOc+rUKfx+53PGtm1ycnLIycmhuro6fsGxvVkdBsPrr7/OK6+8El8eiUTiHwiJySxqOwlVROIJLzGWN954g8cff5za2lqnPu7W0yORCPX19SldeIxZv309Q/KHkDUoi6xBWeTk5ZA+MJ0Jkybw6huv8k3TcS0dwI42H82HT4dZu3YtN9xwQ7y8E3vPIpFIynEppbyl0wQuIiNEZJOI7BSRD0TkHrf9RyJyRES2u3/O+e4Zy7JaHBVXV1WzY8cOKisrKS0tZdasWZSUlLBy5UoqKipYt25d/MJje9MhMzIy2LFjR4vZHrHyQiKfz+eUTaLNiTwxlsOHD7Np0yb27t3b4vW2bZOdnd2lfkYiETIzM3n55Zf58MMP2b9/P+Xl5axcuZIpU6a0e3TeIt6EGrjBKeXcfffdLF68mG9961u89dZbnDhxggEDnIu4vTldVCnVO1IpoUSA7xlj/iYiWcBWEYnd/vcfxph/765gko9ibduOzxrJy8vjyiuv5Morr6SwsJBDhw7xyCOPEAqFmDdvXstySIJTp06xdetWXnvtNUpKSjh06BDjx49nwIAB7k6J/x0/8o5NJUzYZCAY4ODBg5SWllJcXAw46/t8Purq6lqUWzqTl5fH8OHDSU9PJzs7m0AgQHZ2dnx2i5XCiVHi2UFWZhYbN25k+fLlVFZWUltby4IFCygoKGDVqlVMmTIl5diUUt7RaQI3xlQCle7jehHZBRT0RDDJNfDJUyYzPDqccDhMSUkJ06dPj69TUVHB4cOHef3115k3b16bNfCY9957j9tvv52MjAw++eQT/vznPzN16tR2Z2Uk1slj2ywcWUjesDzefvttjh49yvDhwxER/H5/fC56qo4dO8bx48cRkfjRfOKZhJHOt5X4gWVZFiNHjmTZsmXx7R09epT77ruPxYsXs3btWiZOnAgDUg5RKeUBXaqBi8goYArEb5X8toi8JyIrRWTQuQZj23aLRCgi1NTUUF9fj2VZ+P1+AoEAgUCAoqIiFi5cyOHDh9m5c2e7NehwOMycOXMoKytj9erV/OAHPyA/P7/TmnU0GiUxJ+fl5TFixAj27dtHaWkpIkIkEqGxsZFAIODMCkmRiTobrq+vp7a2llWrVrFmzRoOHjzo1PM7udKYfHYAEAwGCQaD+P1+/H4/Q4cOZfXq1Xzuc59jyZIlXYpPKeUNKSdwEckE1gHfNcbUAU8CY4DJOEfoj7fzurtEZIuIbKmu7vhOxeRphHbEpqKigkOHDrUopwCEQiEKCwsJhUKsW7eu7QRloKmpiezsbCZOnMhll13GvffeywUXXNAqgccuYibOnU5c57PPPqO+vp4DBw7wi1/8ghdeeIGGhgbeeustfD4ffl/qE3peeeUVKisrWbx4MdOnT+f++++nvLycM2fOpDTdzxjTZn9t27lrNXYRMy0tjXvvvZdt27bR1NTUpbMEpVT/l1LWEZEATvJ+3hjzWwBjzLGE5Sug7W+QMsY8BTwFznehdLSf5PLBzCtmMnPUTC666KL4rIqEfeL3+7Esi4aGhjYvTCIwcOBANmzYwLFjxxg0aBANDQ38/ve/p7i4mMiESPwdiM3eSEzaLerMWVlceOGFfPGLX2TixIk8/fTT8YuXkUjEuTMyxYkoxdOKKR1YygMPPEB+fj7p6emMGzcuPtUxFcn9jcUdDAYB4hcux44di2VZrF+/nsiiCART3oVSqp/rNIGLkxmeBnYZY55IaM936+MAC4D3zzWY5Jkf4y4ax1VFV+Hz+fj444/54x//SEVFBWPGjCE/P5/S0lL279/PLbfc4pQ82sjhxcXFPPPMM9xwww1EIhFEhHA4zPe//30iF0VavAPJN78Yq7kGLiJkZ2dz+eWXc9NNN/HUU0+xYsUKioqKOHLkSJcuYhYWFmKMITMzk4KCAgKBAHV1dZw4cYJhw4ZxJu9M/Hb7VN4ncEpFwWCQM2fOYFkWPp8PYwyBQIBIJBKf/qiUOn+kcgR+OfD3wA4R2e62/Stwi4hMxqkUHwQ6n7zcieQLkX6/n1AgFL+x5sSJE5SVlfHiiy+SmZmJZVnMmzePSy+9lAarodX2RIS5c+dSU1PDpk2bGDx4MF/60pcoKiqisLCwxVF9JBKJfw9LbC548q30I0eOpKCggEmTJnHnnXeyYsUK3nzzTcaPH084HE75IuGIESO4+OKLeeKJJ0hLS4t/FUAkEuGuu+6iYWEDdHBFwRjjHPEnfGb4/X5qa2upqanh3XffpbGxkQEDBrBu3TqGDRtGSUkJPr/eS6/U+SSVWSj/Q9vFgVe7O5jkurRYzpGmZVnk5uYyZ84cRo8eTWNjo5PcQyHGjRvH4MGDabQaW28PoaCggFtvvZXZs2eTkZHB4MGDyc3NBVrezShWwswTWs9JLxxZyPz580lPTycUCvGFL3yB22+/naFDhzJ69OgW2+pMTk4OS5cuZevWrWRmZpKXl0dTUxMNDQ0UFxfzl/S/pP6m4Vxw/eCDD1i6dCmDBg2iqqoKy7JoamoiNzeXZcuWMWbMmLbLTEopz+p/t9In5PDlspz1rHeepIE9xsYe5Vy8iyV7n8+H5bNopHUCj0qUb/i+gbnAwAXE79qMJbI66uLr+ixf/OtpW9wY5MazOWszD2Q+0BxruuF08WmOFhxlS9oWNvg2pNzP7/i/g/V3FicnnCQYDJKenk4kEiEcDrMxtJHtwe2dbiP5K29zcnIoLi6mqKiIgQMHxm+nz8/P5/Of/zxpaWmdzm5RSnlLv0rgyUfg5e5/8bzjp0sRGwzPy/Mtzx/aO1AWOkxwe9jDHtnTYn1CwOjU44l5SV5yLiYOTWgMQqo/kJNcA2+SJpblL6P6H6v5KPsj0tPTW9yMFPvel8/ks64Hq5Tqt/pdAr+TO6miigMcOKttpJHG9/gej/BIyq+5m7u5gNZTC6/jOraznT/xp5S35cfPwzzcqv1hHuYn/IQIqX83yRVcwXVc1+l6YQmzPLAcLkholHYeK6XOG/0qgQPcwR000cRe9p7V69NJZylLaaD1Rc32/JAfMoxhrdqv5VpsbKaQ+q3oAQI8yIOt2peylDrqOvyptWRzmMOX+XKby3LJ5TZuYxWrUt5ezG3cRi65XX6dUqp/6Xe/ialSV00193N/l1/3GI8xhCE9EJFSqid44jcxVdcMYQirWd3XYSil+ojOK1NKKY/SBK6UUh6lCVwppTxKE7hSSnmUJnCllPIoTeBKKeVRmsCVUsqjNIErpZRH9eqdmCJSD+zutR32vDzg074OohudT/05n/oC2p/+rDf6UmiMaXX7dG/fibm7rdtBvUpEtmh/+qfzqS+g/enP+rIvWkJRSimP0gSulFIe1dsJ/Kle3l9P0/70X+dTX0D705/1WV969SKmUkqp7qMlFKWU8qheS+Aicq2I7BaRfSKypLf2251E5KCI7BCR7SKyxW3LFZEyEdnr/j2or+Nsi4isFJEqEXk/oa3N2MXxc3es3hORqX0Xedva6c+PROSIOz7bRWRuwrIfuv3ZLSJt/8xRHxGRESKySUR2isgHInKP2+7J8emgP14dnzQReVtE3nX782O3fbSIbHbj/o2IBN32Ae7zfe7yUT0WnDGmx//g/JTwh0ARzs/3vgtM6I19d3M/DgJ5SW2PAkvcx0uAf+vrONuJ/QpgKvB+Z7EDc4HXcH5NswTY3Nfxp9ifHwH3tbHuBPff3ACcn6H+EPD1dR8S4ssHprqPs4A9bsyeHJ8O+uPV8REg030cADa77/t/ATe77cuBf3If/zOw3H18M/Cbnoqtt47ALwX2GWP2G2PCwFpgfi/tu6fNB55xHz8DfLUPY2mXMeZPQE1Sc3uxzweeNY7/BXJEJL93Ik1NO/1pz3xgrTHmtDHmALAP599kv2CMqTTG/M19XA/sAgrw6Ph00J/29PfxMcaY2I/sBtw/BpgFvOS2J49PbNxeAq6W5F9M7ya9lcALgMMJzz+m4wHtrwzwhohsFZG73LZhxphK9/FRaOPXkfuv9mL38nh92y0rrEwoZ3mmP+7p9hScozzPj09Sf8Cj4yMiPhHZDlQBZThnCSeNMRF3lcSY4/1xl9cCg3siLr2I2TUzjDFTgeuAfxGRKxIXGuecyZPTerwce4IngTHAZKASeLxvw+kaEckE1gHfNcbUJS7z4vi00R/Pjo8xxjbGTAYuxDk7uLiPQwJ6L4EfAUYkPL/QbfMUY8wR9+8q4Hc4A3ksdvrq/l3VdxF2WXuxe3K8jDHH3P/RosAKmk/D+31/RCSAk+yeN8b81m327Pi01R8vj0+MMeYksAm4DKd0Ffs6ksSY4/1xl2cDx3sint5K4O8AY92rtkGcwv4femnf3UJEMkQkK/YYmAO8j9OPW93VbgX+u28iPCvtxf4H4B/c2Q4lQG3CqXy/lVQHXoAzPuD052Z3dsBoYCzwdm/H1x63Pvo0sMsY80TCIk+OT3v98fD4DBGRHPdxCJiNU9ffBNzkrpY8PrFxuwl40z2D6n69eCV3Ls7V6A+BB3prv90YfxHOlfJ3gQ9ifcCpbW0E9gIbgNy+jrWd+F/AOW09g1Ovu6O92HGuuv/SHasdwLS+jj/F/qxx430P53+i/IT1H3D7sxu4rq/jT+rLDJzyyHvAdvfPXK+OTwf98er4TAK2uXG/DzzkthfhfNDsA14EBrjtae7zfe7yop6KTe/EVEopj9KLmEop5VGawJVSyqM0gSullEdpAldKKY/SBK6UUh6lCVwppTxKE7hSSnmUJnCllPKo/wMYzCZi5EaIvAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "letter,image = get_letters(\"/content/train_v2/train/TRAIN_00003.jpg\")\n",
        "word = get_word(letter)\n",
        "print(word)\n",
        "plt.imshow(image)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "id": "sZwl5csqCo3i",
        "outputId": "afcfad76-350b-4b5b-f900-53415e66bc83"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "LIL0U\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f1e145a2850>"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "letter,image = get_letters(\"/content/train_v2/train/TRAIN_00023.jpg\")\n",
        "word = get_word(letter)\n",
        "print(word)\n",
        "plt.imshow(image)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "id": "wKpX4Ct6CtC_",
        "outputId": "72b420d0-0680-47af-c681-ff4464c592b8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "00PP\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f1e14591f10>"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "letter,image = get_letters(\"/content/validation_v2/validation/VALIDATION_0005.jpg\")\n",
        "word = get_word(letter)\n",
        "print(word)\n",
        "plt.imshow(image)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OX_kIJ_lC1pY"
      },
      "source": [
        "## **Deep Learning: CNN**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YpLd7ShwCxKW"
      },
      "outputs": [],
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), padding = \"same\", activation='relu', input_shape=(32,32,1)))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.25))\n",
        " \n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(35, activation='softmax'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ejro2W0vIL-3"
      },
      "outputs": [],
      "source": [
        "model.compile(loss='categorical_crossentropy', optimizer=\"adam\",metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OmVswpgAC-Iy",
        "outputId": "b4e95875-fdd1-4d3d-da4f-b9cd6ccf5883"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 32, 32, 32)        320       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 16, 16, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 14, 14, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 7, 7, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 5, 5, 128)         73856     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 2, 2, 128)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 2, 2, 128)         0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 512)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               65664     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 35)                4515      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 162,851\n",
            "Trainable params: 162,851\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gwEQvBF9DB_e",
        "outputId": "6cd37012-8246-4b52-fb01-a61504bbaab6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/34\n",
            "4375/4375 [==============================] - 250s 57ms/step - loss: 0.5872 - accuracy: 0.8116 - val_loss: 0.3491 - val_accuracy: 0.8669\n",
            "Epoch 2/34\n",
            "4375/4375 [==============================] - 265s 61ms/step - loss: 0.3357 - accuracy: 0.8858 - val_loss: 0.2798 - val_accuracy: 0.9030\n",
            "Epoch 3/34\n",
            "4375/4375 [==============================] - 278s 64ms/step - loss: 0.2930 - accuracy: 0.8986 - val_loss: 0.2580 - val_accuracy: 0.9177\n",
            "Epoch 4/34\n",
            "4375/4375 [==============================] - 279s 64ms/step - loss: 0.2684 - accuracy: 0.9061 - val_loss: 0.2461 - val_accuracy: 0.9113\n",
            "Epoch 5/34\n",
            "4375/4375 [==============================] - 273s 62ms/step - loss: 0.2542 - accuracy: 0.9113 - val_loss: 0.2728 - val_accuracy: 0.8976\n",
            "Epoch 6/34\n",
            "4375/4375 [==============================] - 271s 62ms/step - loss: 0.2399 - accuracy: 0.9153 - val_loss: 0.2254 - val_accuracy: 0.9221\n",
            "Epoch 7/34\n",
            "4375/4375 [==============================] - 273s 62ms/step - loss: 0.2301 - accuracy: 0.9181 - val_loss: 0.2174 - val_accuracy: 0.9268\n",
            "Epoch 8/34\n",
            "4375/4375 [==============================] - 277s 63ms/step - loss: 0.2225 - accuracy: 0.9210 - val_loss: 0.2217 - val_accuracy: 0.9187\n",
            "Epoch 9/34\n",
            "4375/4375 [==============================] - 273s 62ms/step - loss: 0.2160 - accuracy: 0.9219 - val_loss: 0.2124 - val_accuracy: 0.9296\n",
            "Epoch 10/34\n",
            "4375/4375 [==============================] - 272s 62ms/step - loss: 0.2105 - accuracy: 0.9235 - val_loss: 0.2452 - val_accuracy: 0.9105\n",
            "Epoch 11/34\n",
            "4375/4375 [==============================] - 272s 62ms/step - loss: 0.2049 - accuracy: 0.9248 - val_loss: 0.2288 - val_accuracy: 0.9173\n",
            "Epoch 12/34\n",
            "4375/4375 [==============================] - 258s 59ms/step - loss: 0.2002 - accuracy: 0.9268 - val_loss: 0.2279 - val_accuracy: 0.9213\n",
            "Epoch 13/34\n",
            "4375/4375 [==============================] - 261s 60ms/step - loss: 0.1971 - accuracy: 0.9271 - val_loss: 0.2252 - val_accuracy: 0.9212\n",
            "Epoch 14/34\n",
            "4375/4375 [==============================] - 266s 61ms/step - loss: 0.1930 - accuracy: 0.9282 - val_loss: 0.2387 - val_accuracy: 0.9192\n",
            "Epoch 15/34\n",
            "4375/4375 [==============================] - 263s 60ms/step - loss: 0.1904 - accuracy: 0.9297 - val_loss: 0.2537 - val_accuracy: 0.9068\n",
            "Epoch 16/34\n",
            "4375/4375 [==============================] - 269s 62ms/step - loss: 0.1875 - accuracy: 0.9305 - val_loss: 0.2324 - val_accuracy: 0.9193\n",
            "Epoch 17/34\n",
            "4375/4375 [==============================] - 267s 61ms/step - loss: 0.1840 - accuracy: 0.9311 - val_loss: 0.2275 - val_accuracy: 0.9224\n",
            "Epoch 18/34\n",
            "4375/4375 [==============================] - 263s 60ms/step - loss: 0.1814 - accuracy: 0.9318 - val_loss: 0.2225 - val_accuracy: 0.9242\n",
            "Epoch 19/34\n",
            "4375/4375 [==============================] - 274s 63ms/step - loss: 0.1801 - accuracy: 0.9326 - val_loss: 0.2277 - val_accuracy: 0.9234\n",
            "Epoch 20/34\n",
            "4375/4375 [==============================] - 268s 61ms/step - loss: 0.1781 - accuracy: 0.9335 - val_loss: 0.2235 - val_accuracy: 0.9256\n",
            "Epoch 21/34\n",
            "4375/4375 [==============================] - 266s 61ms/step - loss: 0.1762 - accuracy: 0.9338 - val_loss: 0.2454 - val_accuracy: 0.9148\n",
            "Epoch 22/34\n",
            "4375/4375 [==============================] - 273s 62ms/step - loss: 0.1748 - accuracy: 0.9344 - val_loss: 0.2408 - val_accuracy: 0.9145\n",
            "Epoch 23/34\n",
            "4375/4375 [==============================] - 277s 63ms/step - loss: 0.1729 - accuracy: 0.9348 - val_loss: 0.2563 - val_accuracy: 0.9147\n",
            "Epoch 24/34\n",
            "4375/4375 [==============================] - 271s 62ms/step - loss: 0.1725 - accuracy: 0.9344 - val_loss: 0.2489 - val_accuracy: 0.9180\n",
            "Epoch 25/34\n",
            "4375/4375 [==============================] - 268s 61ms/step - loss: 0.1719 - accuracy: 0.9349 - val_loss: 0.2328 - val_accuracy: 0.9265\n",
            "Epoch 26/34\n",
            "4375/4375 [==============================] - 267s 61ms/step - loss: 0.1693 - accuracy: 0.9358 - val_loss: 0.2478 - val_accuracy: 0.9205\n",
            "Epoch 27/34\n",
            "4375/4375 [==============================] - 269s 62ms/step - loss: 0.1710 - accuracy: 0.9355 - val_loss: 0.2318 - val_accuracy: 0.9253\n",
            "Epoch 28/34\n",
            "4375/4375 [==============================] - 280s 64ms/step - loss: 0.1677 - accuracy: 0.9367 - val_loss: 0.2625 - val_accuracy: 0.9136\n",
            "Epoch 29/34\n",
            "4375/4375 [==============================] - 272s 62ms/step - loss: 0.1659 - accuracy: 0.9371 - val_loss: 0.2471 - val_accuracy: 0.9262\n",
            "Epoch 30/34\n",
            "4375/4375 [==============================] - 269s 62ms/step - loss: 0.1661 - accuracy: 0.9365 - val_loss: 0.2382 - val_accuracy: 0.9237\n",
            "Epoch 31/34\n",
            "4375/4375 [==============================] - 268s 61ms/step - loss: 0.1633 - accuracy: 0.9377 - val_loss: 0.2511 - val_accuracy: 0.9222\n",
            "Epoch 32/34\n",
            "4375/4375 [==============================] - 267s 61ms/step - loss: 0.1650 - accuracy: 0.9374 - val_loss: 0.2481 - val_accuracy: 0.9213\n",
            "Epoch 33/34\n",
            "4375/4375 [==============================] - 270s 62ms/step - loss: 0.1628 - accuracy: 0.9383 - val_loss: 0.2514 - val_accuracy: 0.9211\n",
            "Epoch 34/34\n",
            "4375/4375 [==============================] - 270s 62ms/step - loss: 0.1623 - accuracy: 0.9388 - val_loss: 0.2639 - val_accuracy: 0.9211\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(train_X,train_Y, epochs=34, batch_size=32, validation_data = (val_X, val_Y),  verbose=1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(val_X)"
      ],
      "metadata": {
        "id": "2_kRooJL7IF4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9JSLjg6iu_2q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Evaluation:**"
      ],
      "metadata": {
        "id": "SRappDPK6mRa"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EeEkWU8IDHQT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "4c7469e7-ee9a-48be-96ba-bef9512511cb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "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": {
        "id": "dTz4ok_ZDLgJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "5968646c-e2c0-4527-bdcb-6e894e63866b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "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": {
        "id": "PAd2BnYxDQ0O"
      },
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "041G6RQHDWHa"
      },
      "outputs": [],
      "source": [
        "def get_letters(img):\n",
        "    letters = []\n",
        "    image = cv2.imread(img)\n",
        "    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
        "    ret,thresh1 = cv2.threshold(gray ,127,255,cv2.THRESH_BINARY_INV)\n",
        "    dilated = cv2.dilate(thresh1, None, iterations=2)\n",
        "\n",
        "    cnts = cv2.findContours(dilated.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": "GwMlqu_sDdRL"
      },
      "outputs": [],
      "source": [
        "def get_word(letter):\n",
        "    word = \"\".join(letter)\n",
        "    return word"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RCbP9LE6Dj4V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 132
        },
        "outputId": "15736687-724c-498a-fdef-0268c12afb99"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BCHFS\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f947b981d90>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "letter,image = get_letters(\"/content/train_v2/train/TRAIN_00003.jpg\")\n",
        "word = get_word(letter)\n",
        "print(word)\n",
        "plt.imshow(image)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p5rBJ5ZLDn1G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "8d33e36f-fba0-4cfe-bd4c-8e505bdc89f3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LIL0L\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f947813bc50>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "letter,image = get_letters(\"/content/train_v2/train/TRAIN_00023.jpg\")\n",
        "word = get_word(letter)\n",
        "print(word)\n",
        "plt.imshow(image)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CEIJ2chyDrKz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "9e5c0aec-8c9e-481f-f75f-7097bc54b553"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7HFFFUX\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f9479694710>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FDz74gO7u7qh/3W9fjWaiktIORL8IlHBfmB/+6w+57rrrWLZsGWVlZb5RGNAfreLXmi2YVsCiFYtoppmWQAvNqpnC+YV053bz2uHXOC2xSarccizLSgibM00T27Zpbm6OfnbrLyIJIydd7YwXp5KSEnJzc31DFQfCECPBV7xgwQKOHj3KyZMno+fGPU9+TwCWZUXjzL02Z2ZmcvLkyWgoo0t7ezsvvPACWVlZjq/+Ajrrhmn+4he/oKSkhB/84AeUl5fT1NQUjeBJiDZCD6HXTGxStgXuJqCK71gLpgXZtGkTNTU10VGTKMclMlBZ8SKzPbidiqAnx7cJrIi8gL/n72PKsGyno840TdIk1mUTTHNazE1NTdFWt3vzABIGzYStMLZE/N+e5bW1tRQWFkbL8Mvh4oefC0UpxQsvvEBpaSkLFiyIeRLp6+vDDtj+ouupqxhO5EhNTQ1HjhyJjn5taWlh586dbN68mT/+8Y/k5eX5Dgzy2tvY2MiWLVt4/fXX2b59OzNnzmT+/Pns2rWLp556iu9///u+o0Mt2xr0edBoxhspK+DJOsV6pIfrr7y+XxwjIyL9oids5Qj3SAzDNg0zOu+kQsUI1pSCKcydO5fOzs6YcD/AN4wwYAYQJQmhjytXrmRWyayYZFmD8QH7tajr6ur46f6fcvbsWTo7O6P5RNwnhZ6CHvD0HbpD1r0+cBEhFAqxYMECHn/8cXJycigtLWXv3r1s2bKFe++9l9ra2gv6v0+cOMGOHTv47W9/y89//nPKy8sxDIPVq1fT0tLC73//e1auXMk1i6+JM6w/3lwPpddMRFJWwIGoLzlGnAS6ze4L7usNQXPL2al2crvczlvy1pDq8Zj9GMtZ3n+TiNORcDiMbdvk5+fHDFmPdvjFh9iJf+jftyu+TVYgC1PMaN5sP3rpjfns58+eN28e5eXl7Nu3j8mTJ7NixQrOnTvHgw8+yMKFCzl7w9kYAU8Wb20YBl/60pd47bXXuP/++8nIyGDWrFmsWbOGuro60tPTnSeAAaaGO3jwIAVnC1i3bh3XXntt9KZaXl7OkiVLOHPmDI8++miigCv/HC0azUThggIuIqXAFqAIp125WSn1byJyP/B14FRk0/sis9SPCl5BuY3b6KGHJ3hiUPtuZCNXqiv707ZGWsE1dg0bzY18aH3ouF08whCT6yPSwnbzZ9+gbqBQCpMeb8qUKdTW1lJdXR0Vbe9ozHj/tLJVtHXp5VDo0KDsi8dP6DMyMli6dCnbt2/nN7/5DQcOHCArKysaTpidnR1bpyThhYZhMGPGDL72ta9x+PDhaIbFK664guLi4v5QxwGc4KdOOl+hL37xizFD+0OhEDU1NfT29nLw4EEnf7nXrST9TzC69a2ZiAymBR4GvqWUahCRbGCXiLwQWfdjpdT/uXTVS473B3sDN2Bg0M2FW94Af8ffUUxxtGXqza+9hjVYyskJ4h0JGd/iVaj+CSEkbvaYODE5Ofkk70x+B8CZrk0RnRgC4ChHY7Z361VEEStZyR/4w6Ds8uNGbmQa02iiKfYYSrF48WKam5v5y1/+wsGDB5kzZw5Llizh8ssvJzs9279Aj2lt0sZWtRWVruj9fC/5HfnOHJ0ZfexL28c+tQ8bG0MMjsmxpHXs7ulGRLjsssucQ3hGl06bNo2MjAxKS0sTq+KJ5ddoJiIXFHCl1AngROR9m4i8C0y/1BUbCL/W1rLI36CJnyshLqf4hY4ZH40xEE/JUzzFU56d8X/vKU8QFrGIR3iEq7l6wPIH4mEeZgYzeJVXY5Z/JB+RPTWbz9z2Gao/V01LSwvzqudhBkyajWa68Ax8Ev8+h2Mc4xa5xbEhRMzw/ahtg2gY5+bkkpmZyalTp6KTUrjnNy0tjeLiYqc17+MK0pM5aCYyQ/KBi0g5cCXwOnAdcLeIrAfqcVrpZ3322QDOFOllZWUXWd3UwcQkzMBZ+PyIdzUUU8wxkrdeB1lowucFZiQPQGbkddmFi1FKxTw5DBcDw5msIsIXbvoC96h7MAyDPrvPEWQ7kiNFi7NGk5RBx4GLSBbwFPA/lVLngYeACmAhTgv9//rtp5TarJSqVUrVFhYm9xOPJ4IEaaedEkqGvO9RjrKExPkwxxrXTVFPPbdz+7DLuYIrOMnJmGX/YPwD2WY2mZJJtpFNlmSRZWSRSSaT4v7aaLsoOzSa8cSgWuAiEsQR7/9SSj0NoJRq9qx/GHj2ktQwBRGEdNL5E38aciu8mOLEuTBHgPVqPcUU82X58pD2q6WWX/LL6OcgQf6Zf+Y7fGdY9QgRIp3YsEJLrIRp6DQazYUZTBSKAP8BvKuU+pFneUnEPw6wGhj+VOXjlCqqxroKgNN6LqCAZSxjs9ocm5gqLsWuN4ZdEEqkhDnMifE/T4v8DZc++niYh/km36STzmGVcSu3cgd3DLsOGs14YDAt8OuAdcBbIrInsuw+4FYRWYjzc28C7vTfXTPWuO6PqTKVO7jDiQyJtPKVnTiphDfiJvp/BH3RQYLcwR28qd6kg46E9X4jY+NZz3qWsnTE6qTRpCKDiUJ5Gf+f0qjFfGsujvg4ae/cld4UsPETZPjliBlJIX9IPRRTRzemO2yFCUjgktw8NJrxREqPxNQMDm/yrPj82e5rLGa1iaYRiHsfDAajyavc+TU1Gk0iMpqDIETkFNABnL7QtuOAKUwMO0HbOl7Rtn5yuEwplRDGN6oCDiAi9Uqp2lE96BgwUewEbet4Rdv6ySel84FrNBrNREYLuEaj0aQoYyHgm8fgmGPBRLETtK3jFW3rJ5xR94FrNBqNZmTQLhSNRqNJUUZNwEVkhYg0isj7IvK90TruaCEiTSLylojsEZH6yLJ8EXlBRA5G/k8e63oOBxF5REROisjbnmW+tonDpsh13icii8au5kMnia33i8iHkWu7R0RWedb974itjSJy49jUeniISKmI7BSRd0Rkv4h8M7J83F3bAWxN7WvrDu64lC+cKYEPAbOANGAvMG80jj1aL5x0AlPilv0Q+F7k/feAB8e6nsO07XpgEfD2hWwDVgF/wBm9+1fA62Nd/xGw9X7g2z7bzot8l9OBmZHvuDnWNgzB1hJgUeR9NvBexKZxd20HsDWlr+1otcCvAd5XSh1WSvUCTwJ1o3TssaQOeDTy/lHgi2NYl2GjlPp/QEvc4mS21QFblMNfgDwRGXpe3TEiia3JqAOeVEr1KKWOAO/jfNdTAqXUCaVUQ+R9G+BO1jLuru0AtiYjJa7taAn4dIiZleA4YzyrzyVAAdtFZFdkEguAItWfsfFjnHlFxwvJbBuv1/ruiNvgEY8rbNzYGjdZy7i+tnG2QgpfW92JOXJ8Wim1CFgJfENErveuVM5z2bgM+RnPtkUY1OQlqYrPZC1Rxtu1He7ENJ9URkvAPwS8s9LOiCwbNyilPoz8PwlsxXncanYfMSP/TyYvIeVIZtu4u9ZKqWallKWUsoGH6X+UTnlb/SZrYZxe22QT06TytR0tAX8TqBSRmSKSBqwFto3SsS85IpIpItnue+BzOBNcbIPo/GO3A8+MTQ0vCcls2wasj0Qs/BXQ6nkcT0ni/LzeyUu2AWtFJF1EZgKVwBujXb/hIuI/WQvj8NomszXlr+0o9gKvwun5PQRsHOve2xG2bRZOj/VeYL9rH1AAvAQcBF4E8se6rsO07wmcx8s+HF/g/0hmG06Ews8i1/ktoHas6z8Ctj4WsWUfzg+7xLP9xoitjcDKsa7/EG39NI57ZB+wJ/JaNR6v7QC2pvS11SMxNRqNJkXRnZgajUaTomgB12g0mhRFC7hGo9GkKFrANRqNJkXRAq7RaDQpihZwjUajSVG0gGs0Gk2KogVco9FoUpT/D8/bxP28f6I9AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "letter,image = get_letters(\"/content/train_v2/train/TRAIN_00030.jpg\")\n",
        "word = get_word(letter)\n",
        "print(word)\n",
        "plt.imshow(image)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VkXXNjrbDvCz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "a5ce8e73-fa0d-4e1c-aa0d-980eab20de67"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RUPP\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f947968bad0>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "letter,image = get_letters(\"/content/validation_v2/validation/VALIDATION_0005.jpg\")\n",
        "word = get_word(letter)\n",
        "print(word)\n",
        "plt.imshow(image)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}