Understanding Crossword Puzzles with OpenCV, OCR, and DNNs


Recently I was given the task of creating an algorithm, to extract all possible metadata from the crossword photo. This seemed like an interesting task for me, so I decided to give it a try. These are the topics that will be covered in this blogpost:

  • Crossword cells detection and extraction with OpenCV
  • Crossword cell classification with Pytorch CNN
  • Cell metadata extraction

You can find the full code implementation on my website and my Github.

Crossword cells detection

First things first, to extract the metadata, you have to understand where it is located. For this purpose, I used simple OpenCV heuristics to identify the lines on the crossword puzzle and to form a cell grid out of these lines. The input image needs to be sufficiently large, so all lines could be detected easily.

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The input image and output lines on the same image

Afterward, for cell detection, I found the intersection between lines and formed the cells based on intersection points.

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Image lines intersection points

Finally, at this stage, each cell is cut from the image and saved as a separate file for further manipulations.

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Cells, extracted from a single image

Crossword cell classification with PyTorch CNN

For cell classification, everything was really straightforward. The problem was modeled as a multiclass classification problem with the following targets:

{0: 'both', 1: 'double_text', 2: 'down', 3: 'inverse_arrow', 4: 'other', 5: 'right', 6: 'single_text'}

For each of the target classes, I labeled manually around 100 cells for each class. Afterward, I fitted a simple PyTorch CNN model with the following architecture:

class Net(nn.Module):
# Pytorch CNN model class
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 3)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 3)

self.conv3 = nn.Conv2d(16, 32, 5)
self.conv4 = nn.Conv2d(32, 64, 5)

self.dropout = nn.Dropout(0.3)

self.fc1 = nn.Linear(64*11*11, 512)
self.bnorm1 = nn.BatchNorm1d(512)

self.fc2 = nn.Linear(512, 128)
self.bnorm2 = nn.BatchNorm1d(128)

self.fc3 = nn.Linear(128, 64)
self.bnorm3 = nn.BatchNorm1d(64)

self.fc4 = nn.Linear(64, 7)
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.pool(F.relu(self.conv2(x)))

x = F.relu(self.conv3(x))
x = self.pool(F.relu(self.conv4(x)))

x = x.view(-1, 64*11*11)
x = self.dropout(x)
x = F.relu(self.bnorm1(self.fc1(x)))
x = F.relu(self.bnorm2(self.fc2(x)))
x = F.relu(self.bnorm3(self.fc3(x)))
x = self.fc4(x)
return x

The resulting model predictions were almost descent and generalized well even on crossword puzzles of different formats.

Cell metadata extraction

My final step was to extract all metadata from the labeled cells. For this purpose, I firstly created a classified representation of each image cell in the Pandas DataFrame format.

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Image cells textual representation

Finally, based on the cell class, I either extracted text from the image using Pytesseract, or I extracted arrow coordinates and direction if the cell was classified as one of the arrow cells.

The resulting output of the script looked the following way in JSON format:

[{“label”: “F Faitune |”, “position”: [0, 2], “solution”:{“startPosition”: [0, 3], “direction”: “down”}},
{“label”: “anceur”, “position”: [0, 4], “solution”: {“startPosition”: [1, 4], “direction”: “down”}}]


This work was a great experience for me and offered a great opportunity to dive into a task which was a mix of simple OpenCV heuristics along with usage of more cutting edge concepts like OCR and DNNs for image classification. Thank you for your read!

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