What happens if we encounter a suitable font for our design work but do not
know its name? Visual Font Recognition (VFR) systems are used to identify the
font typeface in an image. These systems can assist graphic designers in
identifying fonts used in images. A VFR system also aids in improving the speed
and accuracy of Optical Character Recognition (OCR) systems. In this paper, we
introduce the first publicly available datasets in the field of Persian font
recognition and employ Convolutional Neural Networks (CNN) to address this
problem. The results show that the proposed pipeline obtained 78.0% top-1
accuracy on our new datasets, 89.1% on the IDPL-PFOD dataset, and 94.5% on the
KAFD dataset. Furthermore, the average time spent in the entire pipeline for
one sample of our proposed datasets is 0.54 and 0.017 seconds for CPU and GPU,
respectively. We conclude that CNN methods can be used to recognize Persian
fonts without the need for additional pre-processing steps such as feature
extraction, binarization, normalization, etc.

本文介绍了公开可用的第一个波斯字体识别数据库，并使用卷积神经网络解决了这个问题。结果表明，提出的流程在新的数据集上获得了 78.0% 的前 1 精度，在 IDPL-PFOD 数据集上为 89.1%，在 KAFD 数据集上为 94.5%。此外，对于我们的数据集的一个样本，整个流程在 CPU 上平均耗时 0.54 秒，在 GPU 上为 0.017 秒。我们得出结论，CNN 方法可以用于识别波斯字体，无需额外的预处理步骤，如特征提取、二值化、标准化等。