A deep convolutional neural network (CNN) is used for the font recognition. To have both a robust and proven network architecture, we used one inspired by a residual network with 50 layers, also known as ResNet50 (He et al. 2016). A typical ResNet50 has layers with a large amount of neurons (from 64 to 512 in the convolutional layers), which can lead to overfitting the training data. To avoid this pitfall, we restrict the layers to having 96 neurons, with a penultimate fully-connected layer of 384 neurons. The training is done by stochastic gradient descent on batches of 64 samples, with a constant momentum of 0.9 and initial learning rate and weight decay of respectively 0.01 and 0.0005. The learning rate and weight decay are divided by 10 every 300,000 samples. The training is stopped after processing a million samples because the error stagnates.