Recognizing Handwriting Words Using PyTorch: A Step-By-Step Guide

PyTorch is a popular open-source machine learning library developed by Facebook’s AI Research lab. It provides various tools and modules to build and train deep learning models. In this tutorial, we will learn how to perform handwriting words recognition using PyTorch. Specifically, we will build a model that can recognize handwritten words from images.

Step 1: Install PyTorch
Before we can start building our model, we need to install PyTorch. PyTorch can be easily installed using pip. You can install PyTorch by running the following command:

pip install torch torchvision

Step 2: Prepare the Dataset
For this tutorial, we will use the IAM Handwriting database, which contains handwritten words by different writers. You can download the dataset from the official website of the IAM Handwriting Database. Once you have downloaded the dataset, you can extract it to a directory of your choice.

Step 3: Preprocess the Dataset
Next, we need to preprocess the dataset. We will use the torchvision.transforms module to apply transformations to the images. In this case, we will resize the images and convert them to grayscale. Here is the code to preprocess the dataset:

import torchvision.transforms as transforms
from torchvision import datasets

transform = transforms.Compose([
    transforms.Resize((32, 128)),
    transforms.Grayscale(),
    transforms.ToTensor()
])

train_dataset = datasets.ImageFolder(root='path/to/iam_database', transform=transform)

Step 4: Build the Model
Now, we can build our handwriting words recognition model. We will create a simple convolutional neural network (CNN) for this task. Here is the code to build the model:

import torch
import torch.nn as nn
import torch.nn.functional as F

class HandwritingRecognitionModel(nn.Module):
    def __init__(self):
        super(HandwritingRecognitionModel, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
        self.fc1 = nn.Linear(64 * 6 * 28, 128)
        self.fc2 = nn.Linear(128, 26)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 64 * 6 * 28)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

model = HandwritingRecognitionModel()

Step 5: Train the Model
Next, we will train the model on the dataset. We will use the torch.optim module to define an optimizer and the torch.utils.data module to create data loaders. Here is the code to train the model:

import torch.optim as optim
from torch.utils.data import DataLoader

train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

for epoch in range(10):
    for i, (inputs, targets) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

        if i % 100 == 0:
            print(f'Epoch {epoch}, Iteration {i}, Loss: {loss.item()}')

Step 6: Test the Model
Finally, we can test the model on some test images to see how well it performs at recognizing handwritten words. Here is the code to test the model:

test_dataset = datasets.ImageFolder(root='path/to/test_dataset', transform=transform)
test_loader = DataLoader(test_dataset, batch_size=32)

correct = 0
total = 0

model.eval()
with torch.no_grad():
    for inputs, targets in test_loader:
        outputs = model(inputs)
        _, predicted = torch.max(outputs, 1)
        total += targets.size(0)
        correct += (predicted == targets).sum().item()

print(f'Accuracy: {(correct/total)*100}%')

And that’s it! You have successfully built a handwriting words recognition model using PyTorch. You can further improve the model’s performance by experimenting with different architectures, hyperparameters, and training strategies. Happy coding!