Persis: A Persian Font Recognition Pipeline Using Convolutional Neural Networks

TL;DR

This paper presents Persis, a CNN-based Persian font recognition system, introducing new datasets and achieving high accuracy and fast processing times, aiding graphic design and OCR tasks.

Contribution

The paper introduces the first public datasets for Persian font recognition and demonstrates CNN effectiveness without complex pre-processing.

Findings

78.0% top-1 accuracy on new datasets

89.1% accuracy on IDPL-PFOD dataset

94.5% accuracy on KAFD dataset

Abstract

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.