EDA2.ipynb

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   "source": [
    "%pip install --upgrade plotly\n",
    "%pip install kaleido==0.1.0post1"
   ]
  },
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   "execution_count": 3,
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   "source": [
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import numpy as np\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import plotly.io as pio\n",
    "pio.renderers.default = 'notebook'"
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  },
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   "execution_count": 21,
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    "df_tes = pd.read_csv('tesseract_experiment.csv')\n",
    "df_tes['font_name'] = df_tes['font_name'].replace('peignotbold', 'peignot')\n",
    "\n",
    "df_tes['accuracy'] = df_tes['accuracy'].str.replace('%', '')\n",
    "df_tes['accuracy'] = pd.to_numeric(df_tes['accuracy'])\n",
    "df_tes['accuracy'] = df_tes['accuracy'] / 100\n",
    "\n",
    "\n",
    "\n",
    "df_gv = pd.read_csv('google_vision_experiment.csv')\n",
    "df_gv['font_name'] = df_gv['font_name'].replace('peignotbold', 'peignot')\n",
    "\n",
    "df_gv['accuracy'] = df_gv['accuracy'].str.replace('%', '')\n",
    "df_gv['accuracy'] = pd.to_numeric(df_gv['accuracy'])\n",
    "df_gv['accuracy'] = df_gv['accuracy'] / 100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tes.rename(columns={'accuracy': 'accuracy_ts'}, inplace=True)\n",
    "df_gv.rename(columns={'accuracy': 'accuracy_gv'}, inplace=True)\n",
    "\n",
    "merged_df = pd.merge(df_tes, df_gv, on=[col for col in df_tes.columns if col != 'accuracy_ts'])"
   ]
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          "xaxis": {
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         }
        },
        "title": {
         "text": "Underlined Scripts"
        },
        "xaxis": {
         "anchor": "y",
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         "ticktext": [
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         "title": {
          "text": "Font Size"
         }
        },
        "yaxis": {
         "anchor": "x",
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          "text": "Accuracy"
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "scripts_df = merged_df[(merged_df['vox_atypl'] == 'scripts') & (merged_df['underlined'] == True)]\n",
    "\n",
    "# Melting the DataFrame to long format\n",
    "df_melted = scripts_df.melt(id_vars=['font_size'], value_vars=['accuracy_ts', 'accuracy_gv'],\n",
    "                            var_name='accuracy_type', value_name='accuracy_value')\n",
    "\n",
    "# Create the plot using Plotly\n",
    "fig = px.box(df_melted, x='font_size', y='accuracy_value', color='accuracy_type',\n",
    "             color_discrete_map={'accuracy_ts': '#1f77b4', 'accuracy_gv': '#2ca02c'},\n",
    "             title='Underlined Scripts')\n",
    "\n",
    "\n",
    "# Define custom x-tick labels\n",
    "custom_labels = {str(font_size): f'{font_size}' for font_size in df_melted['font_size'].unique()}\n",
    "\n",
    "# Update x-ticks\n",
    "fig.update_xaxes(\n",
    "    tickvals=list(custom_labels.keys()), \n",
    "    ticktext=list(custom_labels.values())\n",
    ")\n",
    "\n",
    "# Update the traces to modify the legend names\n",
    "fig.update_traces(\n",
    "    selector=dict(name='accuracy_ts'),\n",
    "    name='Tesseract'\n",
    ")\n",
    "fig.update_traces(\n",
    "    selector=dict(name='accuracy_gv'),\n",
    "    name='Google Vision OCR'\n",
    ")\n",
    "\n",
    "\n",
    "# Update the background colors\n",
    "fig.update_layout(\n",
    "    plot_bgcolor='lightgray',  # Background color of the plot area\n",
    "    paper_bgcolor='white',  # Background color of the entire figure\n",
    "    xaxis_title='Font Size',\n",
    "    font=dict(size=16),\n",
    "    yaxis_title='Accuracy',\n",
    "    legend_title_text='OCR'\n",
    ")\n",
    "\n",
    "# Show the plot\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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   ],
   "source": [
    "scripts_df = merged_df[(merged_df['vox_atypl'] == 'scripts') & (merged_df['underlined'] == False)]\n",
    "\n",
    "# Melting the DataFrame to long format\n",
    "df_melted = scripts_df.melt(id_vars=['font_size'], value_vars=['accuracy_ts', 'accuracy_gv'],\n",
    "                            var_name='accuracy_type', value_name='accuracy_value')\n",
    "\n",
    "# Create the plot using Plotly\n",
    "fig = px.box(df_melted, x='font_size', y='accuracy_value', color='accuracy_type',\n",
    "             color_discrete_map={'accuracy_ts': '#1f77b4', 'accuracy_gv': '#2ca02c'},\n",
    "             title='Scripts')\n",
    "\n",
    "\n",
    "# Define custom x-tick labels\n",
    "custom_labels = {str(font_size): f'{font_size}' for font_size in df_melted['font_size'].unique()}\n",
    "\n",
    "# Update x-ticks\n",
    "fig.update_xaxes(\n",
    "    tickvals=list(custom_labels.keys()), \n",
    "    ticktext=list(custom_labels.values())\n",
    ")\n",
    "\n",
    "# Update the traces to modify the legend names\n",
    "fig.update_traces(\n",
    "    selector=dict(name='accuracy_ts'),\n",
    "    name='Tesseract'\n",
    ")\n",
    "fig.update_traces(\n",
    "    selector=dict(name='accuracy_gv'),\n",
    "    name='Google Vision OCR'\n",
    ")\n",
    "\n",
    "\n",
    "# Update the background colors\n",
    "fig.update_layout(\n",
    "    plot_bgcolor='lightgray',  # Background color of the plot area\n",
    "    paper_bgcolor='white',  # Background color of the entire figure\n",
    "    xaxis_title='Font Size',\n",
    "    font=dict(size=16),\n",
    "    yaxis_title='Accuracy',\n",
    "    legend_title_text='OCR'\n",
    ")\n",
    "\n",
    "# Show the plot\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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           "bgcolor": "#E5ECF6",
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           "bgcolor": "#E5ECF6",
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        "width": 1000,
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     "metadata": {},
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   ],
   "source": [
    "balzac_df = merged_df[(merged_df['font_name'] == 'balzac')]\n",
    "\n",
    "# Melting the DataFrame to long format\n",
    "df_melted = balzac_df.melt(id_vars=['font_size', 'bold', 'underlined'], value_vars=['accuracy_ts', 'accuracy_gv'],\n",
    "                            var_name='accuracy_type', value_name='accuracy_value')\n",
    "\n",
    "grouped_df = df_melted.groupby(['accuracy_type', 'font_size', 'underlined', 'bold'])['accuracy_value'].mean().reset_index()\n",
    "\n",
    "\n",
    "# Create a figure\n",
    "fig = go.Figure()\n",
    "\n",
    "# Add traces\n",
    "fig.add_trace(go.Bar(\n",
    "    x=grouped_df[(grouped_df['underlined'] == False) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_gv')]['font_size'],\n",
    "    y=grouped_df[(grouped_df['underlined'] == False) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_gv')]['accuracy_value'],\n",
    "    text=grouped_df[(grouped_df['underlined'] == False) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_gv')]['accuracy_value'].round(2),\n",
    "    name='Google Vision OCR - Regular',\n",
    "    textfont=dict(size=10),\n",
    "    marker_color='chartreuse',\n",
    "    textposition='outside',\n",
    "    textangle=270\n",
    "))\n",
    "\n",
    "fig.add_trace(go.Bar(\n",
    "    x=grouped_df[(grouped_df['underlined'] == True) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_gv')]['font_size'],\n",
    "    y=grouped_df[(grouped_df['underlined'] == True) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_gv')]['accuracy_value'],\n",
    "    text=grouped_df[(grouped_df['underlined'] == True) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_gv')]['accuracy_value'].round(2),\n",
    "    name='Google Vision OCR - Underlined',\n",
    "    textfont=dict(size=10),\n",
    "    marker_color='cyan',\n",
    "    textposition='outside',\n",
    "    textangle=270\n",
    "))\n",
    "\n",
    "fig.add_trace(go.Bar(\n",
    "    x=grouped_df[(grouped_df['underlined'] == False) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_ts')]['font_size'],\n",
    "    y=grouped_df[(grouped_df['underlined'] == False) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_ts')]['accuracy_value'],\n",
    "    text=grouped_df[(grouped_df['underlined'] == False) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_ts')]['accuracy_value'].round(2),\n",
    "    name='Tesseract - Regular',\n",
    "    textfont=dict(size=10),\n",
    "    marker_color='firebrick',\n",
    "    textposition='outside',\n",
    "    textangle=270\n",
    "))\n",
    "\n",
    "fig.add_trace(go.Bar(\n",
    "    x=grouped_df[(grouped_df['underlined'] == True) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_ts')]['font_size'],\n",
    "    y=grouped_df[(grouped_df['underlined'] == True) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_ts')]['accuracy_value'],\n",
    "    text=grouped_df[(grouped_df['underlined'] == True) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_ts')]['accuracy_value'].round(2),\n",
    "    name='Tesseract - Underlined',\n",
    "    textfont=dict(size=10),\n",
    "    marker_color='pink',\n",
    "    textposition='outside',\n",
    "    textangle=270\n",
    "))\n",
    "\n",
    "# Update layout for vertical bars\n",
    "fig.update_layout(\n",
    "    title='Balzac',\n",
    "    xaxis=dict(title='Font Size', tickvals=grouped_df['font_size'], ticktext=grouped_df['font_size']),  # updating x-axis\n",
    "    yaxis=dict(title='Mean Accuracy'),  # updating y-axis\n",
    "    font=dict(size=16),\n",
    "    legend=dict(\n",
    "        orientation=\"h\",\n",
    "        yanchor=\"top\",\n",
    "        y=1.2,\n",
    "        xanchor=\"center\",\n",
    "        x=0.5\n",
    "    ),\n",
    "    bargap=0.2,\n",
    "    height=500,\n",
    "    width=1000,\n",
    "    plot_bgcolor='lightgray',  # Background color of the plot area\n",
    "    paper_bgcolor='white'  # Background color of the entire figure\n",
    ")\n",
    "\n",
    "fig.update_yaxes(range=[0, 1.05])  # Update y-axis range if necessary\n",
    "\n",
    "# Show the plot\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "lineal_df = merged_df[(merged_df['vox_atypl'] == 'lineal') & (merged_df['underlined'] == False)]\n",
    "\n",
    "# Melting the DataFrame to long format\n",
    "df_melted = lineal_df.melt(id_vars=['font_size'], value_vars=['accuracy_ts', 'accuracy_gv'],\n",
    "                            var_name='accuracy_type', value_name='accuracy_value')\n",
    "\n",
    "# Create the plot using Plotly\n",
    "fig = px.box(df_melted, x='font_size', y='accuracy_value', color='accuracy_type',\n",
    "             color_discrete_map={'accuracy_ts': '#1f77b4', 'accuracy_gv': '#2ca02c'},\n",
    "             title='Lineal')\n",
    "\n",
    "\n",
    "# Define custom x-tick labels\n",
    "custom_labels = {str(font_size): f'{font_size}' for font_size in df_melted['font_size'].unique()}\n",
    "\n",
    "# Update x-ticks\n",
    "fig.update_xaxes(\n",
    "    tickvals=list(custom_labels.keys()), \n",
    "    ticktext=list(custom_labels.values())\n",
    ")\n",
    "\n",
    "# Update the traces to modify the legend names\n",
    "fig.update_traces(\n",
    "    selector=dict(name='accuracy_ts'),\n",
    "    name='Tesseract'\n",
    ")\n",
    "fig.update_traces(\n",
    "    selector=dict(name='accuracy_gv'),\n",
    "    name='Google Vision OCR'\n",
    ")\n",
    "\n",
    "\n",
    "# Update the background colors\n",
    "fig.update_layout(\n",
    "    plot_bgcolor='lightgray',  # Background color of the plot area\n",
    "    paper_bgcolor='white',  # Background color of the entire figure\n",
    "    xaxis_title='Font Size',\n",
    "    font=dict(size=16),\n",
    "    yaxis_title='Accuracy',\n",
    "    legend_title_text='OCR'\n",
    ")\n",
    "\n",
    "# Show the plot\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "lineal_df = merged_df[(merged_df['vox_atypl'] == 'lineal') & (merged_df['underlined'] == True)]\n",
    "\n",
    "# Melting the DataFrame to long format\n",
    "df_melted = lineal_df.melt(id_vars=['font_size'], value_vars=['accuracy_ts', 'accuracy_gv'],\n",
    "                            var_name='accuracy_type', value_name='accuracy_value')\n",
    "\n",
    "# Create the plot using Plotly\n",
    "fig = px.box(df_melted, x='font_size', y='accuracy_value', color='accuracy_type',\n",
    "             color_discrete_map={'accuracy_ts': '#1f77b4', 'accuracy_gv': '#2ca02c'},\n",
    "             title='Underlined Lineal')\n",
    "\n",
    "\n",
    "# Define custom x-tick labels\n",
    "custom_labels = {str(font_size): f'{font_size}' for font_size in df_melted['font_size'].unique()}\n",
    "\n",
    "# Update x-ticks\n",
    "fig.update_xaxes(\n",
    "    tickvals=list(custom_labels.keys()), \n",
    "    ticktext=list(custom_labels.values())\n",
    ")\n",
    "\n",
    "# Update the traces to modify the legend names\n",
    "fig.update_traces(\n",
    "    selector=dict(name='accuracy_ts'),\n",
    "    name='Tesseract'\n",
    ")\n",
    "fig.update_traces(\n",
    "    selector=dict(name='accuracy_gv'),\n",
    "    name='Google Vision OCR'\n",
    ")\n",
    "\n",
    "\n",
    "# Update the background colors\n",
    "fig.update_layout(\n",
    "    plot_bgcolor='lightgray',  # Background color of the plot area\n",
    "    paper_bgcolor='white',  # Background color of the entire figure\n",
    "    xaxis_title='Font Size',\n",
    "    font=dict(size=16),\n",
    "    yaxis_title='Accuracy',\n",
    "    legend_title_text='OCR'\n",
    ")\n",
    "\n",
    "# Show the plot\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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           "#B6E880",
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          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
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           "align": "left"
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          "hovermode": "closest",
          "mapbox": {
           "style": "light"
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           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
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          },
          "scene": {
           "xaxis": {
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            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
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           "yaxis": {
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            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
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           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
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          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
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          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
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           "zerolinecolor": "white",
           "zerolinewidth": 2
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          "yaxis": {
           "automargin": true,
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            "standoff": 15
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           "zerolinecolor": "white",
           "zerolinewidth": 2
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         }
        },
        "title": {
         "text": "Peignot"
        },
        "width": 1000,
        "xaxis": {
         "ticktext": [
          10,
          10,
          12,
          12,
          14,
          14,
          16,
          16,
          18,
          18,
          20,
          20,
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          22,
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          24,
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          26,
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          28,
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          30,
          32,
          32,
          34,
          34,
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          10,
          12,
          12,
          14,
          14,
          16,
          16,
          18,
          18,
          20,
          20,
          22,
          22,
          24,
          24,
          26,
          26,
          28,
          28,
          30,
          30,
          32,
          32,
          34,
          34
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          10,
          10,
          12,
          12,
          14,
          14,
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          22,
          22,
          24,
          24,
          26,
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          28,
          28,
          30,
          30,
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          32,
          34,
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         "title": {
          "text": "Font Size"
         }
        },
        "yaxis": {
         "range": [
          0,
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         "title": {
          "text": "Mean Accuracy"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "peignot_df = merged_df[(merged_df['font_name'] == 'peignot')]\n",
    "\n",
    "\n",
    "# Melting the DataFrame to long format\n",
    "df_melted = peignot_df.melt(id_vars=['font_size', 'bold', 'underlined'], value_vars=['accuracy_ts', 'accuracy_gv'],\n",
    "                            var_name='accuracy_type', value_name='accuracy_value')\n",
    "\n",
    "grouped_df = df_melted.groupby(['accuracy_type', 'font_size', 'underlined', 'bold'])['accuracy_value'].mean().reset_index()\n",
    "\n",
    "\n",
    "# Create a figure\n",
    "fig = go.Figure()\n",
    "\n",
    "# Add traces\n",
    "fig.add_trace(go.Bar(\n",
    "    x=grouped_df[(grouped_df['underlined'] == False) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_gv')]['font_size'],\n",
    "    y=grouped_df[(grouped_df['underlined'] == False) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_gv')]['accuracy_value'],\n",
    "    text=grouped_df[(grouped_df['underlined'] == False) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_gv')]['accuracy_value'].round(2),\n",
    "    name='Google Vision OCR - Regular',\n",
    "    textfont=dict(size=16),\n",
    "    marker_color='chartreuse',\n",
    "    textposition='outside',\n",
    "    textangle=270\n",
    "))\n",
    "\n",
    "fig.add_trace(go.Bar(\n",
    "    x=grouped_df[(grouped_df['underlined'] == True) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_gv')]['font_size'],\n",
    "    y=grouped_df[(grouped_df['underlined'] == True) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_gv')]['accuracy_value'],\n",
    "    text=grouped_df[(grouped_df['underlined'] == True) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_gv')]['accuracy_value'].round(2),\n",
    "    name='Google Vision OCR - Underlined',\n",
    "    textfont=dict(size=16),\n",
    "    marker_color='cyan',\n",
    "    textposition='outside',\n",
    "    textangle=270\n",
    "))\n",
    "\n",
    "fig.add_trace(go.Bar(\n",
    "    x=grouped_df[(grouped_df['underlined'] == False) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_ts')]['font_size'],\n",
    "    y=grouped_df[(grouped_df['underlined'] == False) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_ts')]['accuracy_value'],\n",
    "    text=grouped_df[(grouped_df['underlined'] == False) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_ts')]['accuracy_value'].round(2),\n",
    "    name='Tesseract - Regular',\n",
    "    textfont=dict(size=16),\n",
    "    marker_color='firebrick',\n",
    "    textposition='outside',\n",
    "    textangle=270\n",
    "))\n",
    "\n",
    "fig.add_trace(go.Bar(\n",
    "    x=grouped_df[(grouped_df['underlined'] == True) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_ts')]['font_size'],\n",
    "    y=grouped_df[(grouped_df['underlined'] == True) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_ts')]['accuracy_value'],\n",
    "    text=grouped_df[(grouped_df['underlined'] == True) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_ts')]['accuracy_value'].round(2),\n",
    "    name='Tesseract - Underlined',\n",
    "    textfont=dict(size=16),\n",
    "    marker_color='pink',\n",
    "    textposition='outside',\n",
    "    textangle=270\n",
    "))\n",
    "\n",
    "# Update layout for vertical bars\n",
    "fig.update_layout(\n",
    "    title='Peignot',\n",
    "    xaxis=dict(title='Font Size', tickvals=grouped_df['font_size'], ticktext=grouped_df['font_size']),  # updating x-axis\n",
    "    yaxis=dict(title='Mean Accuracy'),  # updating y-axis\n",
    "    font=dict(size=16),\n",
    "    legend=dict(\n",
    "        orientation=\"h\",\n",
    "        yanchor=\"top\",\n",
    "        y=1.2,\n",
    "        xanchor=\"center\",\n",
    "        x=0.5\n",
    "    ),\n",
    "    bargap=0.2,\n",
    "    height=500,\n",
    "    width=1000,\n",
    "    plot_bgcolor='lightgray',  # Background color of the plot area\n",
    "    paper_bgcolor='white'  # Background color of the entire figure\n",
    ")\n",
    "\n",
    "fig.update_yaxes(range=[0, 1.05])  # Update y-axis range if necessary\n",
    "\n",
    "# Show the plot\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FONTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotAndSaveByFontName(fontName):\n",
    "    font_df = merged_df[(merged_df['font_name'] == fontName)]\n",
    "\n",
    "    # Melting the DataFrame to long format\n",
    "    df_melted = font_df.melt(id_vars=['font_size', 'bold', 'underlined'], value_vars=['accuracy_ts', 'accuracy_gv'],\n",
    "                                var_name='accuracy_type', value_name='accuracy_value')\n",
    "\n",
    "    grouped_df = df_melted.groupby(['accuracy_type', 'font_size', 'underlined', 'bold'])['accuracy_value'].mean().reset_index()\n",
    "\n",
    "\n",
    "    # Create a figure\n",
    "    fig = go.Figure()\n",
    "\n",
    "    # Add traces\n",
    "    fig.add_trace(go.Bar(\n",
    "        x=grouped_df[(grouped_df['underlined'] == False) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_gv')]['font_size'],\n",
    "        y=grouped_df[(grouped_df['underlined'] == False) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_gv')]['accuracy_value'],\n",
    "        text=grouped_df[(grouped_df['underlined'] == False) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_gv')]['accuracy_value'].round(2),\n",
    "        name='Google Vision OCR - Regular',\n",
    "        textfont=dict(size=10),\n",
    "        marker_color='chartreuse',\n",
    "        textposition='outside',\n",
    "        textangle=270\n",
    "    ))\n",
    "\n",
    "    fig.add_trace(go.Bar(\n",
    "        x=grouped_df[(grouped_df['underlined'] == True) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_gv')]['font_size'],\n",
    "        y=grouped_df[(grouped_df['underlined'] == True) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_gv')]['accuracy_value'],\n",
    "        text=grouped_df[(grouped_df['underlined'] == True) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_gv')]['accuracy_value'].round(2),\n",
    "        name='Google Vision OCR - Underlined',\n",
    "        textfont=dict(size=10),\n",
    "        marker_color='cyan',\n",
    "        textposition='outside',\n",
    "        textangle=270\n",
    "    ))\n",
    "\n",
    "    fig.add_trace(go.Bar(\n",
    "        x=grouped_df[(grouped_df['underlined'] == False) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_ts')]['font_size'],\n",
    "        y=grouped_df[(grouped_df['underlined'] == False) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_ts')]['accuracy_value'],\n",
    "        text=grouped_df[(grouped_df['underlined'] == False) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_ts')]['accuracy_value'].round(2),\n",
    "        name='Tesseract - Regular',\n",
    "        textfont=dict(size=10),\n",
    "        marker_color='firebrick',\n",
    "        textposition='outside',\n",
    "        textangle=270\n",
    "    ))\n",
    "\n",
    "    fig.add_trace(go.Bar(\n",
    "        x=grouped_df[(grouped_df['underlined'] == True) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_ts')]['font_size'],\n",
    "        y=grouped_df[(grouped_df['underlined'] == True) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_ts')]['accuracy_value'],\n",
    "        text=grouped_df[(grouped_df['underlined'] == True) & (grouped_df['bold'] == False) & (grouped_df['accuracy_type'] == 'accuracy_ts')]['accuracy_value'].round(2),\n",
    "        name='Tesseract - Underlined',\n",
    "        textfont=dict(size=10),\n",
    "        marker_color='pink',\n",
    "        textposition='outside',\n",
    "        textangle=270\n",
    "    ))\n",
    "\n",
    "    # Update layout for vertical bars\n",
    "    fig.update_layout(\n",
    "        title=fontName,\n",
    "        xaxis=dict(title='Font Size', tickvals=grouped_df['font_size'], ticktext=grouped_df['font_size']),  # updating x-axis\n",
    "        yaxis=dict(title='Mean Accuracy'),  # updating y-axis\n",
    "        font=dict(size=16),\n",
    "        legend=dict(\n",
    "            orientation=\"h\",\n",
    "            yanchor=\"top\",\n",
    "            y=1.2,\n",
    "            xanchor=\"center\",\n",
    "            x=0.6\n",
    "        ),\n",
    "        bargap=0.2,\n",
    "        height=500,\n",
    "        width=1000,\n",
    "        plot_bgcolor='lightgray',  # Background color of the plot area\n",
    "        paper_bgcolor='white'  # Background color of the entire figure\n",
    "    )\n",
    "\n",
    "    fig.update_yaxes(range=[0, 1.05])  # Update y-axis range if necessary\n",
    "\n",
    "    pio.write_image(fig, os.path.join('plots', f'{fontName}.png'), scale=6, format='png', engine='kaleido')\n",
    "\n",
    "    # Show the plot\n",
    "    fig.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Peignot Bold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "df_tes = pd.read_csv('tesseract_peignot.csv')\n",
    "df_tes['accuracy'] = df_tes['accuracy'].str.replace('%', '')\n",
    "df_tes['accuracy'] = pd.to_numeric(df_tes['accuracy'])\n",
    "df_tes['accuracy'] = df_tes['accuracy'] / 100\n",
    "\n",
    "\n",
    "\n",
    "df_gv = pd.read_csv('google_peignot.csv')\n",
    "df_gv['accuracy'] = df_gv['accuracy'].str.replace('%', '')\n",
    "df_gv['accuracy'] = pd.to_numeric(df_gv['accuracy'])\n",
    "df_gv['accuracy'] = df_gv['accuracy'] / 100\n",
    "\n",
    "\n",
    "df_tes.rename(columns={'accuracy': 'accuracy_ts'}, inplace=True)\n",
    "df_gv.rename(columns={'accuracy': 'accuracy_gv'}, inplace=True)\n",
    "\n",
    "merged_df = pd.merge(df_tes, df_gv, on=[col for col in df_tes.columns if col != 'accuracy_ts'])\n",
    "merged_df['font_name'] = merged_df['font_name'].replace('peignotbold', 'Peignot Bold')\n",
    "\n",
    "plotAndSaveByFontName('Peignot Bold')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perpetua Bold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
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       "                            \n",
       "var gd = document.getElementById('ca0715f3-c1c5-4e29-8747-c9c0b454f79a');\n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tes = pd.read_csv('tesseract_perpetua.csv')\n",
    "df_tes['accuracy'] = df_tes['accuracy'].str.replace('%', '')\n",
    "df_tes['accuracy'] = pd.to_numeric(df_tes['accuracy'])\n",
    "df_tes['accuracy'] = df_tes['accuracy'] / 100\n",
    "\n",
    "\n",
    "\n",
    "df_gv = pd.read_csv('google_perpetua.csv')\n",
    "df_gv['accuracy'] = df_gv['accuracy'].str.replace('%', '')\n",
    "df_gv['accuracy'] = pd.to_numeric(df_gv['accuracy'])\n",
    "df_gv['accuracy'] = df_gv['accuracy'] / 100\n",
    "\n",
    "\n",
    "df_tes.rename(columns={'accuracy': 'accuracy_ts'}, inplace=True)\n",
    "df_gv.rename(columns={'accuracy': 'accuracy_gv'}, inplace=True)\n",
    "\n",
    "merged_df = pd.merge(df_tes, df_gv, on=[col for col in df_tes.columns if col != 'accuracy_ts'])\n",
    "merged_df['font_name'] = merged_df['font_name'].replace('perpetua-bold', 'Perpetua Bold')\n",
    "\n",
    "plotAndSaveByFontName('Perpetua Bold')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Times Bold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tes = pd.read_csv('tesseract_times.csv')\n",
    "df_tes['accuracy'] = df_tes['accuracy'].str.replace('%', '')\n",
    "df_tes['accuracy'] = pd.to_numeric(df_tes['accuracy'])\n",
    "df_tes['accuracy'] = df_tes['accuracy'] / 100\n",
    "\n",
    "\n",
    "\n",
    "df_gv = pd.read_csv('google_times.csv')\n",
    "df_gv['accuracy'] = df_gv['accuracy'].str.replace('%', '')\n",
    "df_gv['accuracy'] = pd.to_numeric(df_gv['accuracy'])\n",
    "df_gv['accuracy'] = df_gv['accuracy'] / 100\n",
    "\n",
    "\n",
    "df_tes.rename(columns={'accuracy': 'accuracy_ts'}, inplace=True)\n",
    "df_gv.rename(columns={'accuracy': 'accuracy_gv'}, inplace=True)\n",
    "\n",
    "merged_df = pd.merge(df_tes, df_gv, on=[col for col in df_tes.columns if col != 'accuracy_ts'])\n",
    "merged_df['font_name'] = merged_df['font_name'].replace('timesbold', 'Times Bold')\n",
    "\n",
    "plotAndSaveByFontName('Times Bold')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trafton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "df_tes = pd.read_csv('tesseract_trafton.csv')\n",
    "df_tes['accuracy'] = df_tes['accuracy'].str.replace('%', '')\n",
    "df_tes['accuracy'] = pd.to_numeric(df_tes['accuracy'])\n",
    "df_tes['accuracy'] = df_tes['accuracy'] / 100\n",
    "\n",
    "\n",
    "\n",
    "df_gv = pd.read_csv('google_trafton.csv')\n",
    "df_gv['accuracy'] = df_gv['accuracy'].str.replace('%', '')\n",
    "df_gv['accuracy'] = pd.to_numeric(df_gv['accuracy'])\n",
    "df_gv['accuracy'] = df_gv['accuracy'] / 100\n",
    "\n",
    "\n",
    "df_tes.rename(columns={'accuracy': 'accuracy_ts'}, inplace=True)\n",
    "df_gv.rename(columns={'accuracy': 'accuracy_gv'}, inplace=True)\n",
    "\n",
    "merged_df = pd.merge(df_tes, df_gv, on=[col for col in df_tes.columns if col != 'accuracy_ts'])\n",
    "merged_df['font_name'] = merged_df['font_name'].replace('trafton-script', 'Trafton')\n",
    "\n",
    "plotAndSaveByFontName('Trafton')"
   ]
  }
 ],
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