Feature extraction from the XKCD handwriting dataset

README.md

This is the code documented in https://pelson.github.io/2017/xkcd_font_merge_then_extract_glyphs/.

handwriting_minimal.png

part2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by using the technique from part one to segment the images into individual strokes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from skimage.color import rgb2gray\n",
    "import scipy.ndimage.measurements\n",
    "from skimage import measure\n",
    "import numpy as np\n",
    "from scipy import ndimage as ndi\n",
    "\n",
    "\n",
    "handwriting_img = plt.imread('handwriting_minimal.png')\n",
    "handwriting_img_gray = rgb2gray(handwriting_img)\n",
    "\n",
    "labels, _ = ndi.label(handwriting_img_gray < 1)\n",
    "\n",
    "stroke_locations = measure.regionprops(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this information, we create an image array that isolates the label, and use the sub-image's bounding box as a lookup:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "bbox_to_stroke_img = {}\n",
    "\n",
    "for stroke in stroke_locations: \n",
    "    # [miny, minx, maxy, maxx] (I don't know why its that way around...)\n",
    "    bbox = stroke.bbox\n",
    "\n",
    "    # Construct a slice that can be used to pick out this bounding box from\n",
    "    # the full image.\n",
    "    full_index = [slice(bbox[0], bbox[2]), slice(bbox[1], bbox[3])]\n",
    "    \n",
    "    # Pick out the sub-image, and take a copy so that we can modify it without\n",
    "    # modifying the original.\n",
    "    stroke_img = handwriting_img[full_index + [Ellipsis]].copy()\n",
    "    \n",
    "    # Using the \"labels\" array, produce a binary mask that is True for every\n",
    "    # pixel that is marked as this label, and False otherwise. \n",
    "    stroke_mask = labels[full_index] == stroke.label\n",
    "\n",
    "    # For each color channel, use the mask to maintain the full image pixels that\n",
    "    # are part of this stroke. Where a pixel remains that is not part of this stroke,\n",
    "    # replace it with 1 (ultimately making it white).\n",
    "    for channel in range(3):\n",
    "        stroke_img[:, :, channel] = np.where(stroke_mask, stroke_img[:, :, channel], 1)\n",
    "        \n",
    "    # Convert the image to an RGB byte array.\n",
    "    stroke_img = (stroke_img * 255).astype(np.uint8)\n",
    "\n",
    "    bbox_to_stroke_img[bbox] = stroke_img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pick off potentially wide, but not high, strokes - these are our dots and lines that we may want to merge together (though not in all cases - we do want some punctuation glyphs to remain!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "stroke_merge_contenders = {}\n",
    "\n",
    "for bbox, img in bbox_to_stroke_img.items():\n",
    "    height = bbox[2] - bbox[0]\n",
    "    width = bbox[3] - bbox[1]\n",
    "    if width < 450 and height < 150:\n",
    "        stroke_merge_contenders[bbox] = img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great. Let's do some work to take a look at these images in the notebook. Essentially, the easiest approach is to convert the images to base64 PNGs, and display them in raw HTML. I also want the images to display inline so that I can show many images at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display, HTML\n",
    "from io import BytesIO\n",
    "import PIL\n",
    "import base64\n",
    "\n",
    "\n",
    "def html_float_image_array(img, downscale=5, style=\"display: inline; margin-right:20px\"):\n",
    "    \"\"\"\n",
    "    Generates a base64 encoded image (scaled down) that is displayed inline.\n",
    "    Great for showing multiple images in notebook output.\n",
    "\n",
    "    \"\"\"\n",
    "    im = PIL.Image.fromarray(img)\n",
    "    bio = BytesIO()\n",
    "    width, height = img.shape[:2]\n",
    "    if downscale and (width > downscale ** 2 and height > downscale ** 2):\n",
    "        im = im.resize([height // downscale, width // downscale],\n",
    "                       PIL.Image.ANTIALIAS)\n",
    "    im.save(bio, format='png')\n",
    "    encoded_string = base64.b64encode(bio.getvalue())\n",
    "    html = ('<img src=\"data:image/png;base64,{}\" style=\"{}\"/>'\n",
    "            ''.format(encoded_string.decode('utf-8'), style))\n",
    "    return html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this new function on our stroke_merge_contenders:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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margin-right:20px\"/><img src=\"data:image/png;base64,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\" style=\"display: inline; margin-right:20px\"/><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAXCAIAAAC57ZmSAAABA0lEQVR4nG2RPYqDUBSFzz1KEgx2lgGLYJr0Fm4mkDqbcCFuIRtJG+wCEQQlCQoWgkRyU7wZZ1BP8ZrvfveHB1U9n8+r1YokSREJw/D1eqFpGt/3AZAc3uPxyMfjURQFAFUFICIikmUZq6pSVRExwFR4nsfL5fJ+v00HAJ/PB0Capnw+n/gX412vV9q2jUlUlcYdxXGcsSEiADabzXiGSd/3vN/vU7Ber1nX9bDMcHkQBD/GAP62WiwW01YAWJbl1BARtm07Ld/v9+z7fgqWyyVnB6jqPAAwA8zfcLfb8TeWZZFUVcuyEMfxyNhut3meo+u60+nkum4URYfDIUmS2+2mql9twn0K17Zk/QAAAABJRU5ErkJggg==\" style=\"display: inline; margin-right:20px\"/><img src=\"data:image/png;base64,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\" style=\"display: inline; margin-right:20px\"/><img src=\"data:image/png;base64,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\" style=\"display: inline; margin-right:20px\"/><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAA4AAAAICAIAAABGc1mbAAAAyElEQVR4nIWOIa6FMBREp7elgQQMCkXyAg42Q9gLC2INTUhIULAEDAKDQTYIgqL3CfKR/x0xYsSZEcy8LMs4jtM0zfN8HMe6rlEUOefO8yyKoizLLMuapkHbtkEQ4BdVVUFrDUBKKaUkIiEEgCcBEJFSiojSNKUkSYQQzrn7vp1zzAzgSQBvH4YhxXH8cx2AtZY+nw8ze54n/nhvPDCzEGLfdxhjlFL/+KSUAOq6FsxsjOn7fhiG67q01r7vW2u3bXvdeZ53XfcFto9aqOvlwJwAAAAASUVORK5CYII=\" style=\"display: inline; margin-right:20px\"/><img src=\"data:image/png;base64,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\" style=\"display: inline; margin-right:20px\"/><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAA4AAAAPCAIAAABbdmkjAAABK0lEQVR4nHWSsarCQBBF78bYpgkYglYp9QsCFkHB1tJvsPITDIL/kyq/IKQJpAgS0gRrwVLc6H3F+PaF6LvFsrOcubMzu+CHtNYkz+dzFEW+7x+PRznHV+50Ok0mE/xqvV43TYOvfqPRCIBt20qpwWAAIAgC9LiyLMfjMQAhAAjtui66XFEUvu93OdkrpVarFUi2bUsyTVPHcQBYlmU4pZSEWZbh+XySrKpK7tf1U0rZtg0gjmOSaNv2fr9Pp9P/uP1+L5VBcrvdSr/oSNIOh4PpBEmS9PxMmgxfa/16vUhiuVz2UOF2ux3Jx+Nhpoley8PhEEAYhlpr4/dGPc8zrrJ6nlfXNUkZzh+a5/lsNjN1F4vF5XL55N7f5Xq9bjYby7Lm8/ntdjOP0tMPPSwyT4VA3o8AAAAASUVORK5CYII=\" style=\"display: inline; margin-right:20px\"/><img src=\"data:image/png;base64,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\" style=\"display: inline; margin-right:20px\"/><img src=\"data:image/png;base64,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\" style=\"display: inline; margin-right:20px\"/><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAsAAAAMCAIAAAA7y9DJAAAA6klEQVR4nHWRIa6FMBBFpy2Yl9QgmxAcC8BgECSwCTTB4FgFshrTbaBZRDWSIJA0CNp5ovnk/Zf/j7wzc+9NBhDRWouIiHgcxzzPbdumaRpFkVIKEcGPt22TUgoh4IM4jo0xgIhKKc65VwkhjDFCCKWUUrosC2itwzAEgCAICCGPAWMMALqug3Ec/Rh+47eFEJBlmfeE/3i9Xn/q/kwIQcuyfFI/YYw555qmgWmaAOArxXcvisIYA/u+c87pD49ZnufneTrnABH7vv9qVlXVuq6IeN83WGuv6xqGIUmSuq6llFpr/wTnHCK+AavNbYlkhH8RAAAAAElFTkSuQmCC\" style=\"display: inline; margin-right:20px\"/><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAsAAAAOCAIAAAB2A3HCAAAA/0lEQVR4nG2RMaqDUBBF75unRLAWhOxA3IYitm5AsBDFNbiNFCmyA/dgFpA9CKIg2IlYqJPi/e8P/pxqmDtz58KAmdd1ZeZpmsIwvF6vSZIw89HHvu/MPI5jEAT4JYqiruvUHLZtG4bBdV0AmqYJIYgIgGmaZVlu24Z9333fB6Dr+uGhaZoq2rZF0zSXy4WIhBD4QEpJREVR4Ha7AVDO/zEMg+q6BnAyOFiWheZ5/qr9cYQ6oZI5jgPLsr5eUckejwfyPBdCSCk/ZV3XicjzPGbG6/VSG1JK5XRMP59PZgYzZ1l2OmHb9v1+V6/5+UtVVWma2rYthIjjuO97ZlbSG80ffv4pxMMlAAAAAElFTkSuQmCC\" style=\"display: inline; margin-right:20px\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(''.join(html_float_image_array(img)\n",
    "                     for bbox, img in stroke_merge_contenders.items())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's take a look at the bounding boxes of these marks, so that we can get some intuition about what we have picked out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dlED+1q5deF+ynf84adX0cf0L4vnnn49cHu+9915kv/r376+dZ2dr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\nUt5URUDkEDRgcbUVQgghhMTBuZaYO3e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M0As4r7vgQd958tPnLFyiy/Dhw3198iNIxnYb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1158cd2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "ax = plt.axes()\n",
    "ax.imshow(handwriting_img)\n",
    "for bbox, img in stroke_merge_contenders.items():\n",
    "    height = bbox[2] - bbox[0]\n",
    "    width = bbox[3] - bbox[1]\n",
    "    rect = mpatches.Rectangle([bbox[1], bbox[0]], width=width, height=height,\n",
    "                                facecolor='none', edgecolor='blue')\n",
    "    ax.add_patch(rect)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks promising. Let's try to merge the chosen images to other, suitably close, strokes. In all situations, we want to allow a reasonable y distance, and a small x distance (e.g. consider the strokes for a speech mark \")."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, define the function that can actually do the merging of two images for us. We will have two image arrays and two bounding boxes, and we want to come up with a single image array and bounding box."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def merge_images(img1, img1_bbox, img2, img2_bbox):\n",
    "    \"\"\"\n",
    "    A function to merge together two images with different bounding boxes.\n",
    "\n",
    "    \"\"\"\n",
    "    # The new image bounding box.\n",
    "    bbox = (min([img1_bbox[0], img2_bbox[0]]),\n",
    "            min([img1_bbox[1], img2_bbox[1]]),\n",
    "            max([img1_bbox[2], img2_bbox[2]]),\n",
    "            max([img1_bbox[3], img2_bbox[3]]))\n",
    "    \n",
    "    # The new image shape.\n",
    "    shape = (bbox[2] - bbox[0], bbox[3] - bbox[1], 3)\n",
    "    \n",
    "    # The slice for image 1 inside of the new image array.\n",
    "    img1_slice = [slice(img1_bbox[0] - bbox[0], img1_bbox[2] - bbox[0]),\n",
    "                  slice(img1_bbox[1] - bbox[1], img1_bbox[3] - bbox[1])]\n",
    "    \n",
    "    # The slice for image 2 inside of the new image array.\n",
    "    img2_slice = [slice(img2_bbox[0] - bbox[0], img2_bbox[2] - bbox[0]),\n",
    "                  slice(img2_bbox[1] - bbox[1], img2_bbox[3] - bbox[1])]\n",
    "\n",
    "    # Construct the new image, and fill it with white.\n",
    "    merged_image = np.empty(shape, dtype=np.uint8)\n",
    "    merged_image.fill(255)\n",
    "    \n",
    "    # Use all of image 1 and just drop it into the correct location within the new image.\n",
    "    merged_image[img1_slice] = img1\n",
    "    \n",
    "    # We can't use the same approach for image 2, as it potentially overlaps with image 1.\n",
    "    # Instead we use the parts of image 2 that aren't at the maximum of each color channel. \n",
    "    merged_image[img2_slice] = np.where(img2 != 255, img2,\n",
    "                                        merged_image[img2_slice])\n",
    "    \n",
    "    return merged_image, bbox"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function probably also exists within skimage. Now that we have the ability to merge together images, let's define some functions that will help us measure the distance between potential images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TestResults(failed=0, attempted=11)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def min_interval_distance(interval_1, interval_2):\n",
    "    \"\"\"\n",
    "    Calculate the distance between two intervals.\n",
    "    \n",
    "       >>> min_interval_distance([0, 1], [2, 3])\n",
    "       1\n",
    "       >>> min_interval_distance([0, 1], [0.5, 3])\n",
    "       0\n",
    "       >>> min_interval_distance([10, 11], [5, 8])\n",
    "       2\n",
    "       >>> min_interval_distance([10, 11], [8, 5])\n",
    "       2\n",
    "\n",
    "    There is so much room for more elegance here, but hey-ho...\n",
    "\n",
    "    \"\"\"\n",
    "    interval_1_sorted = sorted(interval_1)\n",
    "    interval_2_sorted = sorted(interval_2)\n",
    "\n",
    "    min_distance = min([np.abs(i_1 - i_2) for i_1 in interval_1 for i_2 in interval_2])\n",
    "    within_1 = any(interval_1_sorted[0] <= i_2 <= interval_1_sorted[1] for i_2 in interval_2)    \n",
    "    within_2 = any(interval_2_sorted[0] <= i_1 <= interval_2_sorted[1] for i_1 in interval_1)\n",
    "\n",
    "    if within_1 or within_2:\n",
    "        min_distance = 0\n",
    "    return min_distance\n",
    "\n",
    "\n",
    "def max_interval_distance(interval_1, interval_2):\n",
    "    \"\"\"\n",
    "    Calculate the distance between two intervals.\n",
    "    \n",
    "       >>> max_interval_distance([0, 1], [2, 3])\n",
    "       3\n",
    "       >>> max_interval_distance([0, 1], [0.5, 3])\n",
    "       3\n",
    "       >>> max_interval_distance([10, 11], [5, 8])\n",
    "       6\n",
    "       >>> max_interval_distance([10, 11], [8, 5])\n",
    "       6\n",
    "\n",
    "    There is so much room for more elegance here, but hey-ho...\n",
    "\n",
    "    \"\"\"\n",
    "    max_distance = max([np.abs(i_1 - i_2) for i_1 in interval_1 for i_2 in interval_2])\n",
    "    return max_distance\n",
    "\n",
    "\n",
    "def contains(interval_1, interval_2):\n",
    "    \"\"\"\n",
    "    Whether one bounding box entirely contains another\n",
    "    \n",
    "       >>> contains([0, 4], [2, 3])\n",
    "       True\n",
    "       >>> contains([0, 1], [0.5, 3])\n",
    "       False\n",
    "       >>> contains([2, 3], [4, 0])\n",
    "       True\n",
    "    \"\"\"\n",
    "    interval_1_sorted = sorted(interval_1)\n",
    "    interval_2_sorted = sorted(interval_2)\n",
    "\n",
    "    within_1 = all(interval_1_sorted[0] <= i_2 <= interval_1_sorted[1] for i_2 in interval_2)    \n",
    "    within_2 = all(interval_2_sorted[0] <= i_1 <= interval_2_sorted[1] for i_1 in interval_1)\n",
    "    return within_1 or within_2\n",
    "\n",
    "\n",
    "# Check the function actually works...\n",
    "import doctest; doctest.testmod()   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A neat trick from the functions above is that I did all of my development in the notebook by starting with function stubs, docstrings and doctests. Only then did I begin to gradually fill in the functionality. I don't often do development in the notebook, but in situations where it makes sense, the doctest technique I've used is an approach that allows you to write testable code in the notebook fairly easily."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now for the hard work - we want to look at each of the ``stroke_merge_contenders`` and find potential pairs of images that may be merged. No doubt the metrics themselves to determine \"suitable\" pairs will require some development iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from collections import OrderedDict, namedtuple\n",
    "\n",
    "Metric = namedtuple('Metric', ['maxx', 'maxy', 'minx', 'miny', 'withinx', 'withiny'])\n",
    "\n",
    "\n",
    "# Track the images that ultimately got merged.\n",
    "images_that_were_mergers = []\n",
    "\n",
    "# Track the images that didn't find a merge buddy.\n",
    "images_that_didnt_get_merged = []\n",
    "\n",
    "# Take a copy of our existing images - we will remove merged strokes from here, and replace\n",
    "# with the composite.\n",
    "merged_bbox_to_stroke_img = bbox_to_stroke_img.copy()\n",
    "\n",
    "# We could use an RTree for efficient nearest neighbour lookup, but just use a greedy search for now.\n",
    "reduced_stroke_merge_contenders = stroke_merge_contenders.copy()\n",
    "\n",
    "\n",
    "while reduced_stroke_merge_contenders:\n",
    "    bbox, img = reduced_stroke_merge_contenders.popitem()\n",
    "    \n",
    "    # If we have already processed this file (as the right hand pair of a merge) then skip it.\n",
    "    if bbox not in merged_bbox_to_stroke_img:\n",
    "        continue\n",
    "    \n",
    "    # Remove this image from the final glyph set.\n",
    "    merged_bbox_to_stroke_img.pop(bbox, None)\n",
    "\n",
    "    candidates = []\n",
    "    for other_bbox, other_img in list(merged_bbox_to_stroke_img.items()):\n",
    "        if other_bbox == bbox:\n",
    "            # We don't want to merge with ourselves.\n",
    "            continue\n",
    "        \n",
    "        x_range, x_range_other = [other_bbox[1], other_bbox[3]], [bbox[1], bbox[3]]\n",
    "        minx = min_interval_distance(x_range, x_range_other)\n",
    "        miny = min_interval_distance([other_bbox[0], other_bbox[2]], [bbox[0], bbox[2]])\n",
    "        \n",
    "        maxx = max_interval_distance([other_bbox[1], other_bbox[3]], [bbox[1], bbox[3]])\n",
    "        maxy = max_interval_distance([other_bbox[0], other_bbox[2]], [bbox[0], bbox[2]])\n",
    "        \n",
    "        withinx = contains([other_bbox[1], other_bbox[3]], [bbox[1], bbox[3]])\n",
    "        withiny = contains([other_bbox[0], other_bbox[2]], [bbox[0], bbox[2]])\n",
    "        \n",
    "        metric = Metric(minx=minx, miny=miny, maxx=maxx, maxy=maxy, withinx=withinx, withiny=withiny)\n",
    "        \n",
    "        # The condition for which a pair of glyphs will be consiered for merging.\n",
    "        # This took a significant amount of iteration to get a good performance.\n",
    "        if (withinx and withiny) or \\\n",
    "                (((maxx < 250 or minx == 0) and minx < 10) and miny < 50) or \\\n",
    "                (other_bbox in reduced_stroke_merge_contenders and minx == 0 and maxy < 350):    \n",
    "            candidates.append([metric, other_bbox, other_img])\n",
    "\n",
    "    if not candidates:\n",
    "        images_that_didnt_get_merged.append([img, bbox])\n",
    "        # Put the stroke back into the new_bboxes so that we don't loose it.\n",
    "        merged_bbox_to_stroke_img[bbox] = img\n",
    "    else:\n",
    "        # Prefer the candidates with a small maximum x over anything else.\n",
    "        candidates.sort(key=lambda candidate: (candidate[0].maxx, candidate))\n",
    "\n",
    "        metric, other_bbox, other_img = candidates[0]\n",
    "        merged_image, merged_bbox = merge_images(img, bbox, other_img, other_bbox)\n",
    "\n",
    "        # We're done with the other image too - remove it from the results.\n",
    "        merged_bbox_to_stroke_img.pop(other_bbox)\n",
    "\n",
    "        # If the resulting image is *tiny* then consider putting it back\n",
    "        # into the pool of items that may be merged. This really is only of\n",
    "        # value to the sprinkles within the cupcake. \n",
    "        height = merged_bbox[2] - merged_bbox[0]\n",
    "        width = merged_bbox[3] - merged_bbox[1]\n",
    "        if width < 100 and height < 100:\n",
    "            reduced_stroke_merge_contenders[merged_bbox] = merged_image\n",
    "\n",
    "        images_that_were_mergers.append(merged_bbox)\n",
    "        merged_bbox_to_stroke_img[merged_bbox] = merged_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Images that didn't get merged\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------------\n",
      "Images that got merged\n"
     ]
    },
    {
     "data": {
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\" style=\"display: inline; margin-right:20px\"/><img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABEAAAAyCAIAAAA1N6c1AAADGElEQVR4nJVVzUorTRCtru4JI0Ic8Y+RaFAX0bWIoAsfQQUXruIr+BTuslPwAcQHceEqbpRZqBBHBQ0M6ggBf5Lu+haVb5zMTHvvrVVPdZ+qU6drqgURAcDp6WkURUEQxHEcBIExRghBRDMzM67rXl9fh2HY7Xbr9frR0REYY/b39+Gv7ezsDKIoGhoaQkSllJRSSomDJqUUQgAAbzUaDVRKTUxMGGP0/2YGTWvN/AHAGHN1dYWe5/m+DwCI+DsrIhJC3N7eIgC4rsusmEPahBBCCGaIiETUarWAiI6Pj5NDnI2P5lOVSqXDw0PQWn9/fx8cHKytrUkpM4fK5XKpVKpUKuvr61tbW81mk4hEUh8ABEEQRdHU1FQcxwDged7o6OjHx4fv+67rJjL0MVprRCzkkwjAF42IfQwRsZfFYTAv0p6+MGluaTPG2NRXHIZF5GBEVK1WlepvFYC01l9fX9vb21JKx3GUUkqp5eXly8vLpAkyBkR0fn7O7DkKU1pdXbVhEABOTk6SXmQkIj49PRER332GGgJAs9lkxdiEEMaY2dlZXuRLQq11r9fL1zk5Ocna5Lfw7e3t/v6exU1vFAb64favhraWUUpZMWEYvr+/A0CGehRFtjvFTqfT7XbTLga/vLxYtc43Fceu1WqIqLUu0NrGO5N8APP8/AypxkmsXC5bMa1WCwaHDuOXlpYgJ0wfk/y0GdNaW/O8vr5mXBx7cXGxkDMA4M3NTWEwz/OsedrtNqR4CyF4niwsLFjzhGEIuVqFEPlZ94NxHCfv5WFvxRSqOTIy8ls9mW8uwPf98fFxsNVTiJmbm2OGxZjCwec4zi9zGB8eHiClGy/CMGTFi3un0+nkvXEcW4cowD//2GDT2jb4+xjbxi8wLOyRP/ROpVKB1N1x6e12+/Hx0Zptfn4eBu+bM9TrdSLq9XoF78L09HQGw7EvLi6ss6parWafSyGEELVazfou7OzscA3JEObYm5ubNg2AiPb29n5iIALA7u4uvzH5YogIjDGfn5+NRmNlZWVsbGx4eHhjY+Pu7s72MBLRf0TULA2nMZMxAAAAAElFTkSuQmCC\" style=\"display: inline; margin-right:20px\"/><img src=\"data:image/png;base64,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\" style=\"display: inline; margin-right:20px\"/><img src=\"data:image/png;base64,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\" style=\"display: inline; margin-right:20px\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Images that didn't get merged\")\n",
    "display(HTML(''.join(html_float_image_array(img) for img, bbox in images_that_didnt_get_merged)))\n",
    "print('-' * 60)\n",
    "print('Images that got merged')\n",
    "display(HTML(''.join(html_float_image_array(img)\n",
    "                     for bbox, img in merged_bbox_to_stroke_img.items()\n",
    "                     if bbox in images_that_were_mergers)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to double check what didn't get merged against the original image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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K50pjYvvu3btD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Z0fPny4cT8h061ri/veZlIHiNxC58OsiTsytg5IHCKvTgQAv+KKKzwzULXym3auXjJUcYcfOnSo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\n1q1boz47v7t164b58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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115b7e9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "ax = plt.axes()\n",
    "ax.imshow(handwriting_img)\n",
    "for img, bbox in images_that_didnt_get_merged:\n",
    "    height = bbox[2] - bbox[0]\n",
    "    width = bbox[3] - bbox[1]\n",
    "    rect = mpatches.Rectangle([bbox[1], bbox[0]], width=width, height=height,\n",
    "                                facecolor='none', edgecolor='blue')\n",
    "    ax.add_patch(rect)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's take a look at all of the characters that we have produced. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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