module_2-lec3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lecture: The (Py)Tesseract Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "B0P4ehjER8bq"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=1173x430 at 0x29C6D2EA438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We're going to start experimenting with tesseract using just a simple image of nice clean text.\n",
    "# Lets first import Image from PIL and display the image text.png.\n",
    "from PIL import Image\n",
    "\n",
    "image = Image.open(\"text.png\")\n",
    "display(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4w4k52VwR8bu"
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'pytesseract'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-0590a592ec48>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m# Lets import pytesseract and use the dir() fundtion to get a sense of what might be some interesting\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# functions to play with\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mpytesseract\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpytesseract\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'pytesseract'"
     ]
    }
   ],
   "source": [
    "# Great, we have a base image of some big clear text\n",
    "# Lets import pytesseract and use the dir() fundtion to get a sense of what might be some interesting\n",
    "# functions to play with\n",
    "import pytesseract\n",
    "dir(pytesseract)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "dg9KlgaxR8b6",
    "outputId": "ac573a44-d53c-41f6-f566-9a203d94901d"
   },
   "outputs": [],
   "source": [
    "# It looks like there are just a handful of interesting functions, and I think image_to_string\n",
    "# is probably our best bet. Lets use the help() function to interrogate this a bit more \n",
    "help(pytesseract.image_to_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "B_SyR7QfR8b_",
    "outputId": "7cd8e9db-5311-4d7b-acc4-247c17b4ce82"
   },
   "outputs": [],
   "source": [
    "# So this function takes an image as the first parameter, then there are a bunch of optional parameters,\n",
    "# and it will return the results of the OCR. I think it's worth comparing this documentation string\n",
    "# with the documentation we were receiving from the PILLOW module. Lets run the help command on the \n",
    "# Image resize function()\n",
    "help(Image.Image.resize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Notice how the PILLOW function has a bit more information in it. First it's using a specific format\n",
    "# called reStructuredText, which is similar in intent to document markups such as HTML, the language of\n",
    "# the web. The intent is to embed semantics in the documentation itself. For instance, in the resize()\n",
    "# function we see the words \"param size\" with colons surrounding it. This allows documentation engines\n",
    "# which create web docs from source code to link the parameter to the extended docs about that parameter.\n",
    "# In this case the extended docs tell us that the size should be passed as a tuple of width and height.\n",
    "# Notice how the docs for image_to_string, for instance, indicate that there is a \"lang\" parameter we can\n",
    "# use, but then fail to say anything about what that parameter is for or what its format is.\n",
    "#\n",
    "# What this really means is that we need to dig deeper. Here's a quick hack if you want to look at the\n",
    "# source code of a function -- you can use the inspect getsource() command and print the results\n",
    "import inspect\n",
    "src = inspect.getsource(pytesseract.image_to_string)\n",
    "print(src)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# There's actually another way in jupyter, and that's to append *two* question marks to the end of\n",
    "# a given function or module. Other editors have similar features, and is a great reason to use a \n",
    "# software development environment\n",
    "pytesseract.image_to_string??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We can see from the source code that there really isn't much more information about what the parameters\n",
    "# are for this image_to_string function. This is because underneath the pytesseract library is calling a C++\n",
    "# library which does all of the hard work, and the author just passes through all of the calls to the \n",
    "# underlying tesseract executable. This is a common issue when working with python libraries, and it means\n",
    "# we need to do some web sleuthing in order to understand how we can interact with tesseract.\n",
    "#\n",
    "# In a case like this I just googled \"tesseract command line parameters\" and the first hit was what I was\n",
    "# looking for, here's the URL: https://github.com/tesseract-ocr/tesseract/wiki/Command-Line-Usage\n",
    "#\n",
    "# This goes to a wiki page which describes how to call the tesseract executable, and as we read down we see\n",
    "# that we can actually have tesseract use multiple languages in its detection, such as English and Hindi, by\n",
    "# passing them in as \"eng+hin\". Very cool. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# One last thing to mention - the image_to_string() function takes in an \"image\", but the docs don't\n",
    "# really describe what this image is underneath. Is it a string to an image file? A PILLOW image?\n",
    "# Something else?\n",
    "#\n",
    "# Again we have to sleuth (and/or experiment) to understand what we should do. If we look at the source\n",
    "# code for the pytesseract library, we see that there is a function called run_and_get_output(). Here's\n",
    "# a link to that function on the author's github account:\n",
    "# https://github.com/madmaze/pytesseract/blob/d1596f7f59a517ad814b7d810ccdef7d33763221/src/pytesseract.py#L199\n",
    "#\n",
    "# In this function we see that one of the first things which happens is the image is saved through\n",
    "# the save_image() function. Here's that line of code:\n",
    "# https://github.com/madmaze/pytesseract/blob/d1596f7f59a517ad814b7d810ccdef7d33763221/src/pytesseract.py#L116\n",
    "#\n",
    "# And we see there that another function is called, prepare(image), which actually loads the image as a\n",
    "# PILLOW image file. So yes, sending a PIL image file is appropriate use for this function! It sure would\n",
    "# have been useful for the author to have included this information in reStructuredText to help us not have\n",
    "# to dig through the implementation. But, this is an open source project -- maybe you would like to contribute\n",
    "# back better documentation?\n",
    "#\n",
    "# Hint: The doc line we needed was :param image: A PIL Image.Image file or an ndarray of bytes\n",
    "#\n",
    "# In the end, we often don't do this full level of investigation, and we just experiment and try things. It\n",
    "# seems likely that a PIL Image.Image would work, given how well known PIL is in the python world. But still,\n",
    "# as you explore and use different libraries you'll see a breadth of different documentation norms, so it's\n",
    "# useful to know how to explore the source code. And now that you're at the end of this course, you've got\n",
    "# the skills to do so!\n",
    "#\n",
    "# Ok, lets try and run tesseract on this image\n",
    "text = pytesseract.image_to_string(image)\n",
    "print(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Looks great! We see that the output includes new line characters, and faithfully represents the text\n",
    "# but doesn't include any special formatting. Lets go on and look at something with a bit more nuance to it."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Module 2.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}