Persis: A Persian Font Recognition Pipeline Using Convolutional Neural Networks
This work addresses font identification for graphic designers and OCR systems in the Persian language domain, representing an incremental advancement with new datasets.
The paper tackled the problem of Persian font recognition by introducing the first publicly available datasets and using Convolutional Neural Networks, achieving top-1 accuracies of 78.0% on their new datasets, 89.1% on IDPL-PFOD, and 94.5% on KAFD.
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.