zDONOTUSEComputer Vision Project_updated.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HRIS Invoice Recognition Project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# string extraction tools\n",
    "import re\n",
    "from Levenshtein import distance\n",
    "\n",
    "# opencv\n",
    "import cv2\n",
    "\n",
    "# images \n",
    "from wand.image import Image\n",
    "from wand.color import Color\n",
    "try:\n",
    "    from PIL import Image as P_image\n",
    "except ImportError:\n",
    "    import Image as P_image\n",
    "\n",
    "# ocr engine\n",
    "import pytesseract\n",
    "\n",
    "# others\n",
    "from datetime import datetime\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tabulate import tabulate\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'jt' 不是内部或外部命令，也不是可运行的程序\n",
      "或批处理文件。\n"
     ]
    }
   ],
   "source": [
    "!jt -t onedork -f roboto -fs 10 -tfs 11 -T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please change your directory to computer_vision"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prerequisite:\n",
    "\n",
    "- Text extraction process:\n",
    "    1. pdfminer.six pip\n",
    "    2. regex\n",
    "    3. pip install python-Levenshtein\n",
    "\n",
    "\n",
    "  \n",
    "\n",
    "- OCR process\n",
    "    1. PDF to Image:\n",
    "        - ImageMagick windows, put it into path and name it as MAGICK_HOME\n",
    "        - ghostscript windows\n",
    "        - wand pip\n",
    "    2. Opencv\n",
    "    3. tesseract windows\n",
    "    4. pytesseract pip\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Process:\n",
    "\n",
    "- if it's a PDF file?\n",
    "\n",
    "    - If yes, can we extract the text through pdfminer without error?\n",
    "        - if yes, use pdfminer\n",
    "        - if no, we convert it into PNGs, do some opencv processing and start the OCR process\n",
    "<br><br>\n",
    "- We have the text file\n",
    "- We use regex and Levenshtein distance to extract text\n",
    "- We score the output\n",
    "- We go back to the OCR & pdfminder stepes and use another way trying to get a higher score\n",
    "<br><br>\n",
    "\n",
    "\n",
    "----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OCR\n",
    "\n",
    "1. PDF to Image\n",
    "2. Opencv preprocessing\n",
    "3. Tesseract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ocr_process(filename, resolution=450):\n",
    "    \"\"\" Convert a PDF into images, \n",
    "        preprocess them using opencv, \n",
    "        and then feed them into Tesseract ocr engine.\n",
    "    \"\"\"\n",
    "    txt = \"\"\n",
    "    all_pages = Image(filename=filename, resolution=resolution)\n",
    "    for i, page in enumerate(all_pages.sequence):\n",
    "        with Image(page) as img:\n",
    "            img.format = 'png'\n",
    "            img.background_color = Color('white')\n",
    "            img.alpha_channel = 'remove'\n",
    "\n",
    "            image_filename = os.path.splitext(os.path.basename(filename))[0]\n",
    "            image_filename = '{}-{}.png'.format(image_filename, i)\n",
    "            path_filename = os.path.join('converted_image', image_filename)\n",
    "            \n",
    "            try:\n",
    "                os.mkdir('converted_image')\n",
    "            except:\n",
    "                pass\n",
    "            \n",
    "            img.save(filename=path_filename) # save it to the output path\n",
    "            \n",
    "            # 1. 转化为灰度图\n",
    "            im = cv2.imread(path_filename)\n",
    "            im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)\n",
    "            \n",
    "            # 这个在Invoice上用不多。当我们需要做Receipt时，需要这个。\n",
    "            \n",
    "#             # 2. 用adaptive threshold对图像进行二值化处理\n",
    "#             im_inv = cv2.adaptiveThreshold(im_gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,9,2)\n",
    "\n",
    "#             # 3. 进行降噪处理\n",
    "#             kernel = 1/16*np.array([[1,2,1],[2,4,2],[1,2,1]])\n",
    "#             im_blur = cv2.filter2D(im_inv, -1, kernel)\n",
    "            \n",
    "            try:\n",
    "                os.mkdir('preprocessed_image')\n",
    "            except:\n",
    "                pass\n",
    "\n",
    "            # save it to preprocessed_image\n",
    "            path_filename2 = os.path.join('preprocessed_image', image_filename)\n",
    "            \n",
    "            cv2.imwrite(path_filename2,im_gray)\n",
    "            \n",
    "            txt = \"\".join([txt,pytesseract.image_to_string(P_image.open(path_filename2),lang=\"eng\")])\n",
    "            \n",
    "    return txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OpenCV\n",
    "\n",
    "这里有个问题，字体需要是黑色，背景是白色，反过来是不行的。\n",
    "\n",
    "这里简单的threshold不太好。最好的方法是locally adaptive thresholding\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We put the function here for future use...\n",
    "# We do not use it in INVOICE process.\n",
    "\n",
    "def pre_processing(filename):\n",
    "    # 1. 转化为灰度图\n",
    "    im = cv2.imread(directory + 'converted_image/' + filename)\n",
    "    im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)\n",
    "    # 2. 用adaptive threshold对图像进行二值化处理\n",
    "    im_inv = cv2.adaptiveThreshold(im_gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,9,2)\n",
    "\n",
    "    # 3. 进行降噪处理\n",
    "    kernel = 1/16*np.array([[1,2,1],[2,4,2],[1,2,1]])\n",
    "    im_blur = cv2.filter2D(im_inv, -1, kernel)\n",
    "\n",
    "    try:\n",
    "        os.mkdir('preprocessed_image')\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "    # save it to preprocessed_image\n",
    "    cv2.imwrite('preprocessed_image/' + filename,im_inv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tesseract ocr\n",
    "\n",
    "Tesseract result is really satisfying. All we need to do is preprocessing the image and run one line of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Web Benefits Design Technology Center\n",
      "\n",
      "Web Benefits Design Corporation WEB BENEFITS DESIGN ~\n",
      "\n",
      "4725 West Sand Lake Road, Suite 300 SN aL) Qesicey\n",
      "Orlando, Florida 32819 I\n",
      "\n",
      " \n",
      "\n",
      "Bill To: Invoice No.\n",
      "| me 106203\n",
      "AvePoint, Inc.\n",
      "525 Washington Blvd., Suite 1400\n",
      "Jersey City, NJ 07310 3/4/2019 3/19/2019\n",
      "Attn: Melissa Fingerhut Payments\n",
      "§ 1,240.35 $ -1,240.35\n",
      "\n",
      "Item Amount\n",
      "Previous Balance 1,240.35\n",
      "Payments / Credits *Thank You* -1,240.35\n",
      "Past Due Balance 0.00\n",
      "\n",
      " \n",
      "\n",
      "Services for AvePoint, Inc.: 1,241.35\n",
      "Customized Benefits Website - Fee Waived $200/month $ 0.00\n",
      "Benefits Administration [211 @ $4.00] $ 844.00\n",
      "Call Center Year-Round [211 @ $0.85] $ 179.35\n",
      "Discrepancy Report Management [218 @ $0.25] $ 54.50\n",
      "COBRA Administration [218 @ $0.75] $ 163.50\n",
      "\n",
      "Balance Due: |$ 1,241.35\n",
      "\n",
      "Thank you for your business!\n",
      "\n",
      "Please Remit Payment to:\n",
      "\n",
      "Web Benefits Design Corporation\n",
      "4725 West Sand Lake Road, Suite 300\n",
      "Orlando, Florida 32819\n"
     ]
    }
   ],
   "source": [
    "# test codes\n",
    "print(ocr_process('test_image/03-19 AvePoint Inc. Inv 106203.pdf'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PDF Text Extraction\n",
    "\n",
    "The layout is not as good as the OCR output, but the accuracy for words is 100%.\n",
    "\n",
    "**The next task is to know how to get a nice layout using pdfminer.**\n",
    "\n",
    "**IMPORTANT** Change char_margin to 30 if you want to match vat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter\n",
    "from pdfminer.converter import TextConverter\n",
    "from pdfminer.layout import LAParams\n",
    "from pdfminer.pdfpage import PDFPage\n",
    "from io import StringIO\n",
    "\n",
    "def convert_pdf_to_txt(path, line_overlap=0.5, char_margin=5, line_margin=0.5, boxes_flow=0.5):\n",
    "    rsrcmgr = PDFResourceManager()\n",
    "    retstr = StringIO()\n",
    "    codec = 'utf-8'\n",
    "    laparams = LAParams(line_overlap=line_overlap, char_margin=char_margin, line_margin=line_margin, word_margin=0.1, boxes_flow=boxes_flow)\n",
    "    device = TextConverter(rsrcmgr, retstr, codec=codec, laparams=laparams)\n",
    "    fp = open(path, 'rb')\n",
    "    interpreter = PDFPageInterpreter(rsrcmgr, device)\n",
    "    password = \"\"\n",
    "    maxpages = 0\n",
    "    caching = True\n",
    "    pagenos=set()\n",
    "\n",
    "    for page in PDFPage.get_pages(fp, pagenos, maxpages=maxpages, password=password,caching=caching, check_extractable=True):\n",
    "        interpreter.process_page(page)\n",
    "\n",
    "    text = retstr.getvalue()\n",
    "\n",
    "    fp.close()\n",
    "    device.close()\n",
    "    retstr.close()\n",
    "    return text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def amount_checker(regex_findall,distance_str):\n",
    "    '''\n",
    "    This is the function to check for amount,\n",
    "    based on different criteria.\n",
    "    \n",
    "    1. regex criteria\n",
    "    2. levenshtein rating criteria\n",
    "    \n",
    "    return a dataframe\n",
    "    '''\n",
    "    counter = 0\n",
    "    \n",
    "    df = pd.DataFrame(columns=['string','amount','rating'])\n",
    "    \n",
    "    for ind, item in enumerate(regex_findall):\n",
    "        if ('tax' in item.lower()) or ('last' in item.lower()):\n",
    "            del regex_findall[ind]\n",
    "        else:\n",
    "            amount = re.search('[0-9]{1,15}.{1,15}[0-9]{2}',item)\n",
    "            \n",
    "            if amount is not None:\n",
    "            \n",
    "                rating = distance(distance_str, item.lower())\n",
    "\n",
    "                amount = amount.group(0).replace(',','')\n",
    "                \n",
    "                # record it in the dataframe\n",
    "                df.loc[counter] = [item,amount,rating]\n",
    "                df[['amount','rating']] = df.loc[:,['amount','rating']].astype(float)\n",
    "                \n",
    "                # if the amount is 0, we drop it. It's false.\n",
    "                df = df[df['amount'] != 0]\n",
    "                counter += 1\n",
    "\n",
    "# testing code below:\n",
    "#     if len(regex_findall) > 0:\n",
    "#         print(tabulate(df.sort_values(by='rating',ascending=True),headers=('string','amount','rating'),tablefmt='psql'))\n",
    "#                 print('The string is: {}'.format(item))\n",
    "#                 print('The amount is: {}'.format(amount))\n",
    "#                 print('The rating is: {}'.format(rating))\n",
    "#                 print('-'*20)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def leven_amount(txt):\n",
    "    '''\n",
    "    This is a warpper for amount_checker.\n",
    "    In here, three kinds of amount are checked.\n",
    "    '''\n",
    "    amount_str_ls = re.findall('(?<!Tax )(?<!Sub)(?<!Sub )(Total[^0-9]{1,30}[0-9,]*\\.\\d\\d)', txt, re.IGNORECASE)\n",
    "    amount_df = amount_checker(amount_str_ls,\"Total: USD \\$%d.%d\".lower())\n",
    "    \n",
    "    balance_str_ls = re.findall('(?<!Previous )(?<!Prior )(?<!Ending )(?<!Past Due )(Balance[^0-9]{1,30}[0-9,]*\\.\\d\\d)', txt, re.IGNORECASE)\n",
    "    balance_df = amount_checker(balance_str_ls,\"Balance due: USD \\$%d.%d\".lower())\n",
    "\n",
    "    due_str_ls = re.findall('(Amount Due[^0-9]{1,30}[0-9,]*\\.\\d\\d)', txt, re.IGNORECASE)\n",
    "    due_df = amount_checker(due_str_ls,\"Amount due: USD \\$%d.%d\".lower())\n",
    "\n",
    "    # add a column for each one above\n",
    "    if len(amount_df) !=0:\n",
    "        amount_df.loc[:,\"Criteria\"] = \"Amount\"\n",
    "    if len(balance_df) !=0:\n",
    "        balance_df.loc[:,\"Criteria\"] = \"Balance\"\n",
    "    if len(due_df) !=0:\n",
    "        due_df.loc[:,\"Criteria\"] = \"Due\"\n",
    "    \n",
    "    # append them all\n",
    "    df = amount_df.append([balance_df,due_df])\n",
    "    \n",
    "#     print(\"amount analysis end\")\n",
    "    \n",
    "    return df\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "#### Total Amount"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def most_common(lst):\n",
    "    return max(set(lst), key=lst.count)\n",
    "\n",
    "def reg_amount(txt):\n",
    "\n",
    "    # prepare three kinds of amount\n",
    "    amount_due = re.findall('Due[^0-9]*\\$\\s*[0-9,]*\\.\\d\\d', txt, re.IGNORECASE)\n",
    "    amount_pay = re.findall('Pay[^0-9]*\\$\\s*[0-9,]*\\.\\d\\d', txt, re.IGNORECASE)\n",
    "    amount_total = re.findall('Total[^0-9]*\\$\\s*[0-9,]*\\.\\d\\d', txt, re.IGNORECASE)\n",
    "    pure_amount = re.findall('\\$\\s*[0-9,]*\\.\\d\\d', txt, re.IGNORECASE)\n",
    "\n",
    "    # BlaBlaBla\n",
    "    amount_ls = []\n",
    "    counter = 0\n",
    "    \n",
    "    try:\n",
    "        for i in (amount_due, amount_pay, amount_total, pure_amount):\n",
    "            for amount in i:\n",
    "                amount_ls.append(float(amount.replace(',','').split('$')[1]))\n",
    "                counter += 1\n",
    "\n",
    "        print(amount_ls)\n",
    "        print(''*20)\n",
    "\n",
    "        print('The most common amount in the list is: {}'.format(most_common(amount_ls)))\n",
    "        print('The maximum amount in the list is: {}'.format(max(amount_ls)))\n",
    "    except:\n",
    "        print(\"parse amount error\")\n",
    "    \n",
    "#     return max(amount_ls), most_common(amount_ls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Net & Vat\n",
    "\n",
    "<span style='color:purple'>还没有好例子呢。需要继续搞！EMEA需要</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Date\n",
    "- <span style='color:red'>Due date\n",
    "- Invoice date\n",
    "- Bill date\n",
    "- <span style='color:red'>Statement date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from dateutil.parser import parse\n",
    "\n",
    "\n",
    "def regex_date(txt):\n",
    "    \n",
    "    bill_date = re.findall('(?<=Bill Date)[^a-zA-Z]*.*\\d\\d\\d\\d.*(?=\\s)', txt, re.IGNORECASE)\n",
    "    invoice_date = re.findall('(?<=Invoice Date)[^a-zA-Z]*.*\\d\\d\\d\\d.*(?=\\s)', txt, re.IGNORECASE)\n",
    "    try:\n",
    "        for ind, i in enumerate(bill_date):\n",
    "            bill_date[ind] = parse(i.replace(':','').strip()).strftime(\"%m/%d/%Y\")\n",
    "        for ind, i in enumerate(invoice_date):\n",
    "            invoice_date[ind] = parse(i.replace(':','').strip()).strftime(\"%m/%d/%Y\")\n",
    "    except:\n",
    "        print(\"parse date error!\")\n",
    "\n",
    "    print(bill_date)\n",
    "    print(invoice_date)\n",
    "\n",
    "    if len(bill_date) != 0:\n",
    "        print('The most common Bill Date in the list is: {}'.format(most_common(bill_date)))\n",
    "    if len(invoice_date) != 0:\n",
    "        print('The most common Invoice Date in the list is: {}'.format(most_common(invoice_date)))\n",
    "        \n",
    "#     return most_common(bill_date), most_common(invoice_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Invoice # or Account #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def regex_invoice_number(txt):\n",
    "    invoice_number = re.findall('INVOICE[^0-9]*\\d\\d\\d[^a-zA-Z]*', txt, re.IGNORECASE)\n",
    "    account_number = re.findall('Account[^0-9]*\\d\\d\\d[^a-zA-Z]*', txt, re.IGNORECASE)\n",
    "    customer_number = re.findall('Customer[^0-9]*\\d\\d\\d[^a-zA-Z]*', txt, re.IGNORECASE)\n",
    "    ref_number = re.findall('Reference[^0-9]*\\d\\d\\d[^a-zA-Z]*', txt, re.IGNORECASE)\n",
    "    sales_order_number = re.findall('Sales order[^0-9]*\\d\\d\\d[^a-zA-Z]*', txt, re.IGNORECASE)\n",
    "\n",
    "\n",
    "    def number_checker(regex,name):\n",
    "        for ind, i in enumerate(regex):\n",
    "            regex[ind] = re.search(\"\\d.*\\d\", regex[ind]).group(0)\n",
    "\n",
    "        print('{} list: {}'.format(name,regex))\n",
    "\n",
    "        if len(regex) != 0:\n",
    "            print('The most common {} in the list is: {}'.format(name,most_common(regex)))\n",
    "\n",
    "        print('-'*20)\n",
    "        \n",
    "#         return most_common(regex)\n",
    "\n",
    "    number_checker(invoice_number,'invoice number')\n",
    "    number_checker(account_number,'account number')\n",
    "    number_checker(customer_number,'customer number')\n",
    "    number_checker(ref_number,'reference number')\n",
    "    number_checker(sales_order_number,'sales order number')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Vendor Name\n",
    "\n",
    "- Vendor Name\n",
    "- Vendor Address"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Vendor Address"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def regex_vendor_address(txt):\n",
    "    vendor_address = re.findall('.{1,30}\\n\\d{1,30}.{1,30}\\n.{1,30}\\n.{1,15}\\n.{1,30}', txt, re.IGNORECASE)\n",
    "    if len(vendor_address) != 0:\n",
    "        common_address = most_common(vendor_address)\n",
    "        print('-'*20)\n",
    "        print(common_address)\n",
    "        print('-'*20)\n",
    "\n",
    "        vendor_name = common_address.splitlines()[0]\n",
    "        print(vendor_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Vendor Remittance Address"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def regex_remittance(txt):\n",
    "    for i in re.findall('To.*\\n.{1,30}\\n.{1,30}\\n.{1,30}\\n.{1,15}', txt, re.IGNORECASE):\n",
    "        print(i)\n",
    "        print('-'*20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### NS Vendor Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# import pandas as pd\n",
    "# from fuzzywuzzy import fuzz\n",
    "# from fuzzywuzzy import process\n",
    "\n",
    "#     df = pd.read_excel('reference/vendor.xlsx')\n",
    "#     df.dropna(inplace=True)\n",
    "#     df['Full Name'] = df.apply(lambda x: str(x['ID']) + ' ' + x['Name'], axis=1)\n",
    "#     df.set_index('Name',inplace=True)\n",
    "\n",
    "#     fuzz_result = process.extract('Harvard Services Group, Inc.',df.index,limit=3)\n",
    "#     result_ls = []\n",
    "\n",
    "#     for i in fuzz_result:\n",
    "#         result_ls.append(df.loc[i[0],'Full Name'])\n",
    "\n",
    "#     for i in result_ls:\n",
    "#         print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### AvePoint Address"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Description..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Main"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MEMO for pdfminer:\n",
    "\n",
    "char_margin = 5 ~ 100 (100几乎就顶到pdf的头头了)\n",
    "\n",
    "char_margin 的调节可以解决，行内，非box隔开字符的问题，e.g. Total amount\n",
    "\n",
    "line_margin .5 ~ 1，box_flow<=1 可以更好的识别address\n",
    "\n",
    "line_margin = 5 也可以把行都去掉了，把它变小到0.5可以解决box问题\n",
    "\n",
    "boxes_flow = 0.5 ~ 5\n",
    "\n",
    "boxes_flow = 5 可以把所有字符挤在一起\n",
    "\n",
    "box > 1.5 line > 1.5 所有的字儿就变成一行了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def regex_extraction(txt):\n",
    "    '''\n",
    "    This controls what functions will be run.\n",
    "    You can use this to test each function independently.\n",
    "    '''\n",
    "    return leven_amount(txt)\n",
    "#     reg_amount(txt)\n",
    "#     regex_date(txt)\n",
    "#     regex_vendor_address(txt)\n",
    "#     regex_remittance(txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def balance_rating_up(x):\n",
    "    if x['Criteria'] == 'Balance':\n",
    "        return x['rating'] + 2\n",
    "    else:\n",
    "        return x['rating']\n",
    "    \n",
    "def ocr_rating_down(x):\n",
    "    if x['Process'] == 'OCR process':\n",
    "        return x['rating'] - 3\n",
    "    else:\n",
    "        return x['rating']\n",
    "    \n",
    "    \n",
    "def agg_dfs(df):\n",
    "    if len(df) > 1:\n",
    "        df.loc[:,['amount','rating']].astype(float)\n",
    "        agg_df = df.copy()\n",
    "        agg_df['rating'] = df.apply(balance_rating_up,axis=1)\n",
    "        agg_df['rating'] = df.apply(ocr_rating_down, axis=1)\n",
    "\n",
    "        agg_df = (df.groupby('amount')\n",
    "              .aggregate({'string':len,'rating':np.mean})\n",
    "              .sort_values(by='rating',ascending=True)\n",
    "              .reset_index())\n",
    "        agg_df.loc[:,'string'].astype(float)\n",
    "        agg_df['final rating'] = agg_df['rating'] - (agg_df['string'] - 1)\n",
    "        agg_df.drop('rating',axis=1,inplace=True)\n",
    "        return agg_df\n",
    "    else:\n",
    "        return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Process</th>\n",
       "      <th>Criteria</th>\n",
       "      <th>string</th>\n",
       "      <th>amount</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Balance</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>44</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>OCR process</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Process Criteria  string  amount  rating\n",
       "0            2  Balance       1      66       1\n",
       "1            2        2       2      66       2\n",
       "2            2        3       3      44       3\n",
       "3            2        4       4      44       4\n",
       "4            2        5       5      44       5\n",
       "5  OCR process        6       6      33       6"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'Process':['2','2','2','2','2', 'OCR process'],\n",
    "    'Criteria':['Balance',2,3,4,5,6],\n",
    "    'string':[1,2,3,4,5,6],\n",
    "    'amount':[66,66,44,44,44,33],\n",
    "    'rating':[1,2,3,4,5,6]\n",
    "})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Process</th>\n",
       "      <th>Criteria</th>\n",
       "      <th>string</th>\n",
       "      <th>amount</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Balance</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>44</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>OCR process</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Process Criteria  string  amount  rating\n",
       "0            2  Balance       1      66       3\n",
       "1            2        2       2      66       2\n",
       "2            2        3       3      44       3\n",
       "3            2        4       4      44       4\n",
       "4            2        5       5      44       5\n",
       "5  OCR process        6       6      33       3"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rating'] = df.apply(balance_rating_up,axis=1)\n",
    "df['rating'] = df.apply(ocr_rating_down, axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-------------+----------------+\n",
      "|   amount |   frequency |   final rating |\n",
      "|----------+-------------+----------------|\n",
      "|       66 |           2 |            1.5 |\n",
      "|       33 |           1 |            3   |\n",
      "|       44 |           3 |            2   |\n",
      "+----------+-------------+----------------+\n"
     ]
    }
   ],
   "source": [
    "df = (df.groupby('amount')\n",
    "      .aggregate({'string':len,'rating':np.mean})\n",
    "      .sort_values(by='rating',ascending=True)\n",
    "      .reset_index())\n",
    "df['final rating'] = df['rating'] - (df['string'] - 1)\n",
    "df.drop('rating',axis=1,inplace=True)\n",
    "\n",
    "print(tabulate(df,tablefmt='psql',showindex=False,headers=('amount','frequency','final rating')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analysing pdf 03-19 AvePoint Inc. Inv 106203.pdf...\n",
      "\n",
      "Starting PDFminer process...\n",
      "Performing option 1 for pdfminer\n",
      "Performing option 2 for pdfminer\n",
      "Performing option 3 for pdfminer\n",
      "\n",
      "Starting ocr process...\n",
      "\n",
      "The initial rating is:\n",
      "+----+-------------+------------+--------------------------+----------+----------+\n",
      "|    | Process     | Criteria   | string                   |   amount |   rating |\n",
      "|----+-------------+------------+--------------------------+----------+----------|\n",
      "|  0 | OCR process | Balance    | Balance Due: |$ 1,241.35 |  1241.35 |       10 |\n",
      "+----+-------------+------------+--------------------------+----------+----------+\n",
      "\n",
      "The final aggregated rating is:\n",
      "+-------------+---------+--------------------------+-------------+----------------+\n",
      "|             |         | amount                   |   frequency |   final rating |\n",
      "|-------------+---------+--------------------------+-------------+----------------|\n",
      "| OCR process | Balance | Balance Due: |$ 1,241.35 |     1241.35 |             10 |\n",
      "+-------------+---------+--------------------------+-------------+----------------+\n",
      "--------------------\n",
      "\n",
      "Analysing pdf 1-791-81400.pdf...\n",
      "\n",
      "Starting PDFminer process...\n",
      "Performing option 1 for pdfminer\n",
      "Performing option 2 for pdfminer\n",
      "Performing option 3 for pdfminer\n",
      "\n",
      "Starting ocr process...\n",
      "\n",
      "The initial rating is:\n",
      "+----+--------------------+------------+---------------------------------+----------+----------+\n",
      "|    | Process            | Criteria   | string                          |   amount |   rating |\n",
      "|----+--------------------+------------+---------------------------------+----------+----------|\n",
      "|  1 | PDF Miner option 2 | Amount     | Total Charges USD $186.68       |   186.68 |       14 |\n",
      "|  0 | OCR process        | Amount     | Total Charges USD $186.68       |   186.68 |       14 |\n",
      "|  0 | PDF Miner option 1 | Amount     | Total Charges                   |   186.68 |       18 |\n",
      "|    |                    |            | USDUSD                          |          |          |\n",
      "|    |                    |            | $186.68                         |          |          |\n",
      "|  0 | PDF Miner option 2 | Amount     | TOTAL THIS INVOICE USD $186.68  |   186.68 |       19 |\n",
      "|  1 | OCR process        | Amount     | TOTAL THIS INVOICE USD $186.68  |   186.68 |       19 |\n",
      "|  2 | PDF Miner option 2 | Amount     | Total FedEx Express USD $186.68 |   186.68 |       20 |\n",
      "|  2 | OCR process        | Amount     | Total FedEx Express USD $186.68 |   186.68 |       20 |\n",
      "+----+--------------------+------------+---------------------------------+----------+----------+\n",
      "\n",
      "The final aggregated rating is:\n",
      "+----------+-------------+----------------+\n",
      "|   amount |   frequency |   final rating |\n",
      "|----------+-------------+----------------|\n",
      "|   186.68 |           7 |        11.7143 |\n",
      "+----------+-------------+----------------+\n",
      "--------------------\n",
      "\n",
      "Analysing pdf 379160088.pdf...\n",
      "\n",
      "Starting PDFminer process...\n",
      "Performing option 1 for pdfminer\n",
      "Performing option 2 for pdfminer\n",
      "Performing option 3 for pdfminer\n",
      "\n",
      "Starting ocr process...\n",
      "\n",
      "The initial rating is:\n",
      "+----+--------------------+------------+--------------------+----------+----------+\n",
      "|    | Process            | Criteria   | string             |   amount |   rating |\n",
      "|----+--------------------+------------+--------------------+----------+----------|\n",
      "|  0 | PDF Miner option 1 | Amount     | TOTAL DUE$1,063.17 |  1063.17 |       11 |\n",
      "|  0 | PDF Miner option 3 | Amount     | TOTAL DUE$1,063.17 |  1063.17 |       11 |\n",
      "|  0 | OCR process        | Amount     | TOTAL DUE          |  1063.17 |       13 |\n",
      "|    |                    |            |                    |          |          |\n",
      "|    |                    |            | $1,063.17          |          |          |\n",
      "+----+--------------------+------------+--------------------+----------+----------+\n",
      "\n",
      "The final aggregated rating is:\n",
      "+----------+-------------+----------------+\n",
      "|   amount |   frequency |   final rating |\n",
      "|----------+-------------+----------------|\n",
      "|  1063.17 |           3 |        9.66667 |\n",
      "+----------+-------------+----------------+\n",
      "--------------------\n",
      "\n",
      "Analysing pdf 6910722-3962918.pdf...\n",
      "\n",
      "Starting PDFminer process...\n",
      "Performing option 1 for pdfminer\n",
      "Performing option 2 for pdfminer\n",
      "Performing option 3 for pdfminer\n",
      "\n",
      "Starting ocr process...\n",
      "\n",
      "The initial rating is:\n",
      "+----+--------------------+------------+--------------------------------+----------+----------+\n",
      "|    | Process            | Criteria   | string                         |   amount |   rating |\n",
      "|----+--------------------+------------+--------------------------------+----------+----------|\n",
      "|  0 | PDF Miner option 1 | Amount     | Total Due $188.00              |      188 |       10 |\n",
      "|  2 | PDF Miner option 1 | Amount     | Total Due: $188.00             |      188 |       10 |\n",
      "|  0 | PDF Miner option 2 | Amount     | Total Due $188.00              |      188 |       10 |\n",
      "|  1 | PDF Miner option 2 | Amount     | Total Due: $188.00             |      188 |       10 |\n",
      "|  3 | PDF Miner option 2 | Amount     | Total Due: $188.00             |      188 |       10 |\n",
      "|  0 | PDF Miner option 3 | Amount     | Total Due $188.00              |      188 |       10 |\n",
      "|  1 | PDF Miner option 3 | Amount     | Total Due: $188.00             |      188 |       10 |\n",
      "|  0 | OCR process        | Amount     | Total Due: $188.00             |      188 |       10 |\n",
      "|  2 | OCR process        | Amount     | Total Due: $188.00             |      188 |       10 |\n",
      "|  3 | OCR process        | Amount     | TotalDue $188.00               |      188 |       10 |\n",
      "|  1 | PDF Miner option 1 | Amount     | Total Due:                     |      188 |       11 |\n",
      "|    |                    |            | $188.00                        |          |          |\n",
      "|  2 | PDF Miner option 2 | Amount     | Total Current Charges: $188.00 |      188 |       21 |\n",
      "|  1 | OCR process        | Amount     | Total Current Charges: $188.00 |      188 |       21 |\n",
      "+----+--------------------+------------+--------------------------------+----------+----------+\n",
      "\n",
      "The final aggregated rating is:\n",
      "+----------+-------------+----------------+\n",
      "|   amount |   frequency |   final rating |\n",
      "|----------+-------------+----------------|\n",
      "|      188 |          13 |      -0.230769 |\n",
      "+----------+-------------+----------------+\n",
      "--------------------\n",
      "\n",
      "Analysing pdf 73 585 818 06.pdf...\n",
      "\n",
      "Starting PDFminer process...\n",
      "Performing option 1 for pdfminer\n",
      "Performing option 2 for pdfminer\n",
      "Performing option 3 for pdfminer\n",
      "\n",
      "Starting ocr process...\n",
      "\n",
      "The initial rating is:\n",
      "+----+--------------------+------------+----------------------------------------+----------+----------+\n",
      "|    | Process            | Criteria   | string                                 |   amount |   rating |\n",
      "|----+--------------------+------------+----------------------------------------+----------+----------|\n",
      "|  0 | PDF Miner option 2 | Amount     | Totalamountdue $31.42                  |    31.42 |       12 |\n",
      "|  3 | PDF Miner option 2 | Amount     | Totalelectriccharges $31.42            |    31.42 |       20 |\n",
      "|  2 | OCR process        | Amount     | Total electric charges $31.42          |    31.42 |       21 |\n",
      "|  2 | PDF Miner option 2 | Amount     | Totalelectricsupplycharges $20.55      |    20.55 |       25 |\n",
      "|  1 | OCR process        | Amount     | Total electric supply charges $20.55   |    20.55 |       26 |\n",
      "|  1 | PDF Miner option 2 | Amount     | Totalelectricdeliverycharges $10.87    |    10.87 |       27 |\n",
      "|  0 | OCR process        | Amount     | Total electric delivery charges $10.87 |    10.87 |       29 |\n",
      "+----+--------------------+------------+----------------------------------------+----------+----------+\n",
      "\n",
      "The final aggregated rating is:\n",
      "+----------+-------------+----------------+\n",
      "|   amount |   frequency |   final rating |\n",
      "|----------+-------------+----------------|\n",
      "|    31.42 |           3 |        15.6667 |\n",
      "|    20.55 |           2 |        24.5    |\n",
      "|    10.87 |           2 |        27      |\n",
      "+----------+-------------+----------------+\n",
      "--------------------\n",
      "\n",
      "Analysing pdf 731_1722485_usd_20190228_invoice_553369247008.pdf...\n",
      "\n",
      "Starting PDFminer process...\n",
      "Performing option 1 for pdfminer\n",
      "Performing option 2 for pdfminer\n",
      "Performing option 3 for pdfminer\n",
      "\n",
      "Starting ocr process...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The initial rating is:\n",
      "+----+--------------------+------------+---------------------------+----------+----------+\n",
      "|    | Process            | Criteria   | string                    |   amount |   rating |\n",
      "|----+--------------------+------------+---------------------------+----------+----------|\n",
      "|  0 | OCR process        | Amount     | Total USD 199.00          |      199 |        7 |\n",
      "|  0 | PDF Miner option 2 | Amount     | Total USD          199.00 |      199 |       15 |\n",
      "|  0 | PDF Miner option 1 | Amount     | Total                     |       99 |       16 |\n",
      "|    |                    |            | USD                       |          |          |\n",
      "|    |                    |            |           99.00           |          |          |\n",
      "|  0 | PDF Miner option 3 | Amount     | Total                     |       99 |       16 |\n",
      "|    |                    |            | USD                       |          |          |\n",
      "|    |                    |            |           99.00           |          |          |\n",
      "+----+--------------------+------------+---------------------------+----------+----------+\n",
      "\n",
      "The final aggregated rating is:\n",
      "+----------+-------------+----------------+\n",
      "|   amount |   frequency |   final rating |\n",
      "|----------+-------------+----------------|\n",
      "|      199 |           2 |             10 |\n",
      "|       99 |           2 |             15 |\n",
      "+----------+-------------+----------------+\n",
      "--------------------\n",
      "\n",
      "Analysing pdf atlas workbase_invoice_3546.pdf...\n",
      "\n",
      "Starting PDFminer process...\n",
      "Performing option 1 for pdfminer\n",
      "Performing option 2 for pdfminer\n",
      "Performing option 3 for pdfminer\n",
      "\n",
      "Starting ocr process...\n",
      "\n",
      "The initial rating is:\n",
      "+----+--------------------+------------+---------------------+----------+----------+\n",
      "|    | Process            | Criteria   | string              |   amount |   rating |\n",
      "|----+--------------------+------------+---------------------+----------+----------|\n",
      "|  0 | PDF Miner option 2 | Amount     | TOTAL AMOUNT 849.00 |      849 |       12 |\n",
      "|  0 | OCR process        | Amount     | TOTAL AMOUNT 849.00 |      849 |       12 |\n",
      "|  0 | PDF Miner option 1 | Amount     | TOTAL AMOUNT        |      849 |       13 |\n",
      "|    |                    |            | 849.00              |          |          |\n",
      "|  0 | PDF Miner option 3 | Amount     | TOTAL AMOUNT        |      849 |       13 |\n",
      "|    |                    |            | 849.00              |          |          |\n",
      "|  1 | OCR process        | Amount     | TOTAL AMOUNT        |      849 |       26 |\n",
      "|    |                    |            |                     |          |          |\n",
      "|    |                    |            |                     |          |          |\n",
      "|    |                    |            |                     |          |          |\n",
      "|    |                    |            | NET AMOUNT 849.00   |          |          |\n",
      "+----+--------------------+------------+---------------------+----------+----------+\n",
      "\n",
      "The final aggregated rating is:\n",
      "+----------+-------------+----------------+\n",
      "|   amount |   frequency |   final rating |\n",
      "|----------+-------------+----------------|\n",
      "|      849 |           5 |           11.2 |\n",
      "+----------+-------------+----------------+\n",
      "--------------------\n",
      "\n",
      "Analysing pdf B09610077.pdf...\n",
      "\n",
      "Starting PDFminer process...\n",
      "Performing option 1 for pdfminer\n",
      "Performing option 2 for pdfminer\n",
      "Performing option 3 for pdfminer\n",
      "\n",
      "Starting ocr process...\n",
      "\n",
      "The initial rating is:\n",
      "+----+--------------------+------------+------------------------------+----------+----------+\n",
      "|    | Process            | Criteria   | string                       |   amount |   rating |\n",
      "|----+--------------------+------------+------------------------------+----------+----------|\n",
      "|  0 | PDF Miner option 2 | Amount     | Total 7,274.39               |  7274.39 |       11 |\n",
      "|  0 | OCR process        | Amount     | Total 7,274.39               |  7274.39 |       11 |\n",
      "|  0 | PDF Miner option 1 | Amount     | Total                        |  7274.39 |       12 |\n",
      "|    |                    |            | 7,274.39                     |          |          |\n",
      "|  0 | PDF Miner option 2 | Balance    | Balance 6,789.45             |  6789.45 |       15 |\n",
      "|  0 | OCR process        | Balance    | Balance 6,789.45             |  6789.45 |       15 |\n",
      "|  0 | PDF Miner option 1 | Balance    | Balance                      |  6789.45 |       16 |\n",
      "|    |                    |            | 6,789.45                     |          |          |\n",
      "|  0 | PDF Miner option 3 | Balance    | BalanceRecycling FeeCurrency |  7274.39 |       27 |\n",
      "|    |                    |            | 7,274.39                     |          |          |\n",
      "+----+--------------------+------------+------------------------------+----------+----------+\n",
      "\n",
      "The final aggregated rating is:\n",
      "+----------+-------------+----------------+\n",
      "|   amount |   frequency |   final rating |\n",
      "|----------+-------------+----------------|\n",
      "|  7274.39 |           4 |        12.25   |\n",
      "|  6789.45 |           3 |        13.3333 |\n",
      "+----------+-------------+----------------+\n",
      "--------------------\n",
      "\n",
      "Analysing pdf bww_media_group_payment_instructions_2018_pdf.pdf...\n",
      "\n",
      "Starting PDFminer process...\n",
      "Performing option 1 for pdfminer\n",
      "Performing option 2 for pdfminer\n",
      "Performing option 3 for pdfminer\n",
      "\n",
      "Starting ocr process...\n",
      "\n",
      "The initial rating is:\n",
      "+-----------+------------+----------+----------+----------+\n",
      "| Process   | Criteria   | string   | amount   | rating   |\n",
      "|-----------+------------+----------+----------+----------|\n",
      "+-----------+------------+----------+----------+----------+\n",
      "\n",
      "The final aggregated rating is:\n",
      "+----------+-------------+----------------+\n",
      "| amount   | frequency   | final rating   |\n",
      "|----------+-------------+----------------|\n",
      "+----------+-------------+----------------+\n",
      "--------------------\n",
      "\n",
      "Analysing pdf CSCINV20190125_7766149_81107743398.pdf...\n",
      "\n",
      "Starting PDFminer process...\n",
      "Performing option 1 for pdfminer\n",
      "Performing option 2 for pdfminer\n",
      "Performing option 3 for pdfminer\n",
      "\n",
      "Starting ocr process...\n",
      "\n",
      "The initial rating is:\n",
      "+----+--------------------+------------+------------------------+----------+----------+\n",
      "|    | Process            | Criteria   | string                 |   amount |   rating |\n",
      "|----+--------------------+------------+------------------------+----------+----------|\n",
      "|  0 | PDF Miner option 1 | Amount     | Total [USD]40.00       |       40 |        9 |\n",
      "|  0 | PDF Miner option 3 | Amount     | Total [USD]170.00      |      170 |        9 |\n",
      "|  0 | PDF Miner option 2 | Amount     | Total [USD] $ 190.00   |      190 |       10 |\n",
      "|  0 | PDF Miner option 3 | Due        | AMOUNT DUEIAQP$ 190.00 |      190 |       11 |\n",
      "|  1 | PDF Miner option 3 | Due        | AMOUNT DUEIAQP$ 190.00 |      190 |       11 |\n",
      "|  0 | OCR process        | Due        | AMOUNT DUE             |      190 |       11 |\n",
      "|    |                    |            | $ 190.00               |          |          |\n",
      "|  1 | OCR process        | Due        | AMOUNT DUE             |      190 |       11 |\n",
      "|    |                    |            | $ 190.00               |          |          |\n",
      "|  0 | OCR process        | Amount     | Total [USD]            |      190 |       21 |\n",
      "|    |                    |            |                        |          |          |\n",
      "|    |                    |            |                        |          |          |\n",
      "|    |                    |            |                        |          |          |\n",
      "|    |                    |            | Amount                 |          |          |\n",
      "|    |                    |            |                        |          |          |\n",
      "|    |                    |            | $ 190.00               |          |          |\n",
      "+----+--------------------+------------+------------------------+----------+----------+\n",
      "\n",
      "The final aggregated rating is:\n",
      "+----------+-------------+----------------+\n",
      "|   amount |   frequency |   final rating |\n",
      "|----------+-------------+----------------|\n",
      "|       40 |           1 |            9   |\n",
      "|      170 |           1 |            9   |\n",
      "|      190 |           6 |            7.5 |\n",
      "+----------+-------------+----------------+\n",
      "--------------------\n",
      "\n",
      "Analysing pdf CSCINV20190227_7766149_81107879598.pdf...\n",
      "\n",
      "Starting PDFminer process...\n",
      "Performing option 1 for pdfminer\n",
      "Performing option 2 for pdfminer\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing option 3 for pdfminer\n",
      "\n",
      "Starting ocr process...\n",
      "\n",
      "The initial rating is:\n",
      "+----+--------------------+------------+------------------------+----------+----------+\n",
      "|    | Process            | Criteria   | string                 |   amount |   rating |\n",
      "|----+--------------------+------------+------------------------+----------+----------|\n",
      "|  0 | PDF Miner option 3 | Amount     | Total [USD]            |       40 |        9 |\n",
      "|    |                    |            | -40.00                 |          |          |\n",
      "|  0 | PDF Miner option 1 | Amount     | Total [USD]            |      180 |       10 |\n",
      "|    |                    |            | $ 180.00               |          |          |\n",
      "|  0 | PDF Miner option 2 | Amount     | Total [USD] $ 180.00   |      180 |       10 |\n",
      "|  0 | PDF Miner option 3 | Due        | AMOUNT DUEIKVP$ 180.00 |      180 |       11 |\n",
      "|  1 | PDF Miner option 3 | Due        | AMOUNT DUEIKVP$ 180.00 |      180 |       11 |\n",
      "|  0 | OCR process        | Due        | AMOUNT DUE             |      180 |       11 |\n",
      "|    |                    |            | $ 180.00               |          |          |\n",
      "|  1 | OCR process        | Due        | AMOUNT DUE             |      180 |       11 |\n",
      "|    |                    |            | $ 180.00               |          |          |\n",
      "|  0 | OCR process        | Amount     | Total [USD]            |      180 |       21 |\n",
      "|    |                    |            |                        |          |          |\n",
      "|    |                    |            |                        |          |          |\n",
      "|    |                    |            |                        |          |          |\n",
      "|    |                    |            | Amount                 |          |          |\n",
      "|    |                    |            |                        |          |          |\n",
      "|    |                    |            | $ 180.00               |          |          |\n",
      "+----+--------------------+------------+------------------------+----------+----------+\n",
      "\n",
      "The final aggregated rating is:\n",
      "+----------+-------------+----------------+\n",
      "|   amount |   frequency |   final rating |\n",
      "|----------+-------------+----------------|\n",
      "|       40 |           1 |        9       |\n",
      "|      180 |           7 |        6.14286 |\n",
      "+----------+-------------+----------------+\n",
      "--------------------\n",
      "\n",
      "Analysing pdf Dominion - Apt 8C - 2.15.19.pdf...\n",
      "\n",
      "Starting PDFminer process...\n",
      "Performing option 1 for pdfminer\n",
      "Performing option 2 for pdfminer\n",
      "Performing option 3 for pdfminer\n",
      "\n",
      "Starting ocr process...\n",
      "\n",
      "The initial rating is:\n",
      "+----+-------------+------------+------------------------------+----------+----------+\n",
      "|    | Process     | Criteria   | string                       |   amount |   rating |\n",
      "|----+-------------+------------+------------------------------+----------+----------|\n",
      "|  0 | OCR process | Due        | Amount Due: $ 120.17         |   120.17 |        9 |\n",
      "|  1 | OCR process | Balance    | Balance 120.17               |   120.17 |       15 |\n",
      "|  0 | OCR process | Amount     | Total Amount Due: $ 120.17   |   120.17 |       17 |\n",
      "|  1 | OCR process | Amount     | Total Current Charges 120.17 |   120.17 |       20 |\n",
      "+----+-------------+------------+------------------------------+----------+----------+\n",
      "\n",
      "The final aggregated rating is:\n",
      "+----------+-------------+----------------+\n",
      "|   amount |   frequency |   final rating |\n",
      "|----------+-------------+----------------|\n",
      "|   120.17 |           4 |          12.25 |\n",
      "+----------+-------------+----------------+\n",
      "--------------------\n",
      "\n",
      "Analysing pdf FRUS212085-0.pdf...\n",
      "\n",
      "Starting PDFminer process...\n",
      "Performing option 1 for pdfminer\n",
      "Performing option 2 for pdfminer\n",
      "Performing option 3 for pdfminer\n",
      "\n",
      "Starting ocr process...\n",
      "\n",
      "The initial rating is:\n",
      "+----+--------------------+------------+-------------------------------------------+----------+----------+\n",
      "|    | Process            | Criteria   | string                                    |   amount |   rating |\n",
      "|----+--------------------+------------+-------------------------------------------+----------+----------|\n",
      "|  0 | OCR process        | Due        | Amount Due $42,116.88                     |  42116.9 |       11 |\n",
      "|  0 | PDF Miner option 1 | Due        | Amount Due                                |  42116.9 |       27 |\n",
      "|    |                    |            |                     $42,116.88            |          |          |\n",
      "|  0 | PDF Miner option 2 | Due        | Amount Due                     $42,116.88 |  42116.9 |       27 |\n",
      "|  0 | PDF Miner option 3 | Due        | Amount Due                                |  42116.9 |       27 |\n",
      "|    |                    |            |                     $42,116.88            |          |          |\n",
      "+----+--------------------+------------+-------------------------------------------+----------+----------+\n",
      "\n",
      "The final aggregated rating is:\n",
      "+----------+-------------+----------------+\n",
      "|   amount |   frequency |   final rating |\n",
      "|----------+-------------+----------------|\n",
      "|  42116.9 |           4 |             20 |\n",
      "+----------+-------------+----------------+\n",
      "--------------------\n",
      "\n",
      "Analysing pdf Harvard Cleaning Invoice # 999038.pdf...\n",
      "\n",
      "Starting PDFminer process...\n",
      "Performing option 1 for pdfminer\n",
      "Performing option 2 for pdfminer\n",
      "Performing option 3 for pdfminer\n",
      "\n",
      "Starting ocr process...\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-dfd17509b902>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     40\u001b[0m         \u001b[1;31m# **************************************ocr process starts\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'\\nStarting ocr process...'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 42\u001b[1;33m         \u001b[0mtxt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mocr_process\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdir_file\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     43\u001b[0m         \u001b[0mtem_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mregex_extraction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtxt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     44\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-3-9426013175a8>\u001b[0m in \u001b[0;36mocr_process\u001b[1;34m(filename, resolution)\u001b[0m\n\u001b[0;32m     46\u001b[0m             \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_filename2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mim_gray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     47\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 48\u001b[1;33m             \u001b[0mtxt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtxt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpytesseract\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimage_to_string\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mP_image\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_filename2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlang\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"eng\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     49\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mtxt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\computer_vision\\lib\\site-packages\\pytesseract\\pytesseract.py\u001b[0m in \u001b[0;36mimage_to_string\u001b[1;34m(image, lang, config, nice, output_type)\u001b[0m\n\u001b[0;32m    307\u001b[0m         \u001b[0mOutput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDICT\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mlambda\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'text'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mrun_and_get_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    308\u001b[0m         \u001b[0mOutput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSTRING\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mlambda\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mrun_and_get_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 309\u001b[1;33m     }[output_type]()\n\u001b[0m\u001b[0;32m    310\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    311\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\computer_vision\\lib\\site-packages\\pytesseract\\pytesseract.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m()\u001b[0m\n\u001b[0;32m    306\u001b[0m         \u001b[0mOutput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBYTES\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mlambda\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mrun_and_get_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    307\u001b[0m         \u001b[0mOutput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDICT\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mlambda\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'text'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mrun_and_get_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 308\u001b[1;33m         \u001b[0mOutput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSTRING\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mlambda\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mrun_and_get_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    309\u001b[0m     }[output_type]()\n\u001b[0;32m    310\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\computer_vision\\lib\\site-packages\\pytesseract\\pytesseract.py\u001b[0m in \u001b[0;36mrun_and_get_output\u001b[1;34m(image, extension, lang, config, nice, return_bytes)\u001b[0m\n\u001b[0;32m    216\u001b[0m         }\n\u001b[0;32m    217\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 218\u001b[1;33m         \u001b[0mrun_tesseract\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    219\u001b[0m         \u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'output_filename_base'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextsep\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mextension\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    220\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0moutput_file\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\computer_vision\\lib\\site-packages\\pytesseract\\pytesseract.py\u001b[0m in \u001b[0;36mrun_tesseract\u001b[1;34m(input_filename, output_filename_base, extension, lang, config, nice)\u001b[0m\n\u001b[0;32m    186\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mTesseractNotFoundError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    187\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 188\u001b[1;33m     \u001b[0mstatus_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror_string\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mproc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    189\u001b[0m     \u001b[0mproc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    190\u001b[0m     \u001b[0mproc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\computer_vision\\lib\\subprocess.py\u001b[0m in \u001b[0;36mwait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    988\u001b[0m             \u001b[0mendtime\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_time\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    989\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 990\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_wait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    991\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    992\u001b[0m             \u001b[1;31m# https://bugs.python.org/issue25942\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\computer_vision\\lib\\subprocess.py\u001b[0m in \u001b[0;36m_wait\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m   1228\u001b[0m                 \u001b[1;31m# API note: Returns immediately if timeout_millis == 0.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1229\u001b[0m                 result = _winapi.WaitForSingleObject(self._handle,\n\u001b[1;32m-> 1230\u001b[1;33m                                                      timeout_millis)\n\u001b[0m\u001b[0;32m   1231\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0m_winapi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mWAIT_TIMEOUT\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1232\u001b[0m                     \u001b[1;32mraise\u001b[0m \u001b[0mTimeoutExpired\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Main Loop\n",
    "startTime = datetime.now()\n",
    "\n",
    "# set your working directory\n",
    "directory = 'D:/git/Invoice-Receipt-OCR/'\n",
    "dir_test_img = directory + 'test_image/'\n",
    "\n",
    "# reference for pdfminer looping. This matters a lot!\n",
    "argu = [(5, 0.5, 5), (100, 1, 5),(5, 1.5, 1.5)]\n",
    "\n",
    "# main loop\n",
    "for filename in os.listdir(dir_test_img): \n",
    "    dir_file = \"\".join([dir_test_img, filename])\n",
    "    print('Analysing pdf {}...'.format(filename))\n",
    "    \n",
    "    # If it's a pdf file, then...\n",
    "    if filename.endswith(\".pdf\"): \n",
    "        # **************************************pdfminer process starts\n",
    "        print('\\nStarting PDFminer process...')\n",
    "        \n",
    "        counter = 1\n",
    "        df = pd.DataFrame()\n",
    "        \n",
    "        # Perform PDF Miner process:\n",
    "        # Looping three times with different settings\n",
    "        for i, j, k in argu:\n",
    "            print('Performing option {} for pdfminer'.format(counter))\n",
    "            txt = convert_pdf_to_txt(dir_file, char_margin=i, line_margin=j, boxes_flow=k)\n",
    "            tem_df = regex_extraction(txt)\n",
    "            \n",
    "            # Add a column to tem_df to show it's from PDFminer process\n",
    "            if (tem_df is not None) and (len(tem_df) != 0):\n",
    "                tem_df.loc[:,\"Process\"] = \"PDF Miner option {}\".format(counter)\n",
    "            \n",
    "            # Append all DataFrames together\n",
    "            df = df.append(tem_df)\n",
    "            counter += 1\n",
    "        # **************************************pdfminer process ends\n",
    "        \n",
    "        # **************************************ocr process starts\n",
    "        print('\\nStarting ocr process...')\n",
    "        txt = ocr_process(dir_file)\n",
    "        tem_df = regex_extraction(txt)\n",
    "                       \n",
    "        if (tem_df is not None) and (len(tem_df) != 0):\n",
    "            tem_df.loc[:,\"Process\"] = \"OCR process\"\n",
    "        \n",
    "        # Append all DataFrames from the above processes together\n",
    "        df = df.append(tem_df)\n",
    "        \n",
    "        # print initial rating\n",
    "        df = df.loc[:,['Process','Criteria','string','amount','rating']]\n",
    "        print('\\nThe initial rating is:')\n",
    "        print(tabulate(df.sort_values(by='rating'),tablefmt='psql',headers=('Process','Criteria','string','amount','rating')))\n",
    "        \n",
    "        # aggregate ratings based on amount\n",
    "        # do some ajustment on ratings\n",
    "        # print final aggregated rating\n",
    "        agg_df = agg_dfs(df)\n",
    "                       \n",
    "        print('\\nThe final aggregated rating is:')\n",
    "        print(tabulate(agg_df,tablefmt='psql',showindex=False,headers=('amount','frequency','final rating')))\n",
    "        # **************************************ocr process ends\n",
    "\n",
    "        \n",
    "        \n",
    "        \n",
    "    else: # if it's not a pdf file\n",
    "#         txt = ocr_process('test_image/' + filname) # We use a OCR process\n",
    "#         regex_extraction(txt)\n",
    "        pass\n",
    "        \n",
    "    print(\"-\"*20 + \"\\n\")\n",
    "\n",
    "\n",
    "print(datetime.now() - startTime)"
   ]
  }
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