Update

Notebook/Handwitten-Name-Recognition-ANNs.ipynb

@@ -36,7 +36,7 @@
     "# Use:\n",
     "# 'load': If you want to load the datase from the directory\n",
     "# 'download': To download data from the database and process the images\n",
-    "dataset_load_method = 'download'\n",
+    "dataset_load_method = 'load'\n",
     "\n",
     "# Define if you want to save the dataset to a file\n",
     "save_dataset = False\n",
@@ -45,10 +45,10 @@
     "load_classifiers = False\n",
     "\n",
     "# Define if you want to save the trained classifiers to a file\n",
-    "save_classifiers = False\n",
+    "save_classifiers = True\n",
     "\n",
     "# Define if you want to save classification test output to a file\n",
-    "save_results = False\n",
+    "save_results = True\n",
     "if (save_results):\n",
     "    result_output_file = open('result_output.txt','w') \n",
     "\n",
@@ -492,7 +492,12 @@
    "source": [
     "def labelsep(label):\n",
     "    if (type(label) is str or type(label) is np.str_):\n",
-    "        return list(label)\n",
+    "        decomposed_label = list(label)\n",
+    "        labels = []\n",
+    "        for i in range(0, len(decomposed_label)):\n",
+    "            if (decomposed_label[i] != ' '):\n",
+    "                labels.append(decomposed_label[i])\n",
+    "        return labels\n",
     "    else:\n",
     "        return []"
    ]
@@ -731,7 +736,7 @@
     "    if (len(characters) != len(charlabels) or len(characters) == 0 or len(charlabels) == 0):\n",
     "        if (enable_error_output):\n",
     "            print(\"[Warning] Input number \"+str(i)+\" inconsistent! Skipping this one...\")\n",
-    "        Train_with_inconsistencies.extend([i])\n",
+    "        Train_with_inconsistencies.append(i)\n",
     "        z += 1\n",
     "    else:\n",
     "        X_train_chars.extend(characters)\n",
@@ -773,7 +778,7 @@
     "    else:\n",
     "        X_test_chars.extend(characters)\n",
     "        Y_test_chars.extend(charlabels)\n",
-    "        Test_without_inconsistencies.extend([i])\n",
+    "        Test_without_inconsistencies.append(i)\n",
     "\n",
     "print_percentage(100, \"Processing train image \"+ str(len(X_test))+\" :\")\n",
     "print(\"\")\n",