  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.
