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。

Persis: 使用卷积神经网络的波斯字体识别流程

Persis: A Persian Font Recognition Pipeline Using Convolutional Neural  Networks

As font is one of the core design concepts, automatic font identification and
similar font suggestion from an image or photo has been on the wish list of
many designers. We study the Visual Font Recognition (VFR) problem, and advance
the state-of-the-art remarkably by developing the DeepFont system. First of
all, we build up the first available large-scale VFR dataset, named AdobeVFR,
consisting of both labeled synthetic data and partially labeled real-world
data. Next, to combat the domain mismatch between available training and
testing data, we introduce a Convolutional Neural Network (CNN) decomposition
approach, using a domain adaptation technique based on a Stacked Convolutional
Auto-Encoder (SCAE) that exploits a large corpus of unlabeled real-world text
images combined with synthetic data preprocessed in a specific way. Moreover,
we study a novel learning-based model compression approach, in order to reduce
the DeepFont model size without sacrificing its performance. The DeepFont
system achieves an accuracy of higher than 80% (top-5) on our collected
dataset, and also produces a good font similarity measure for font selection
and suggestion. We also achieve around 6 times compression of the model without
any visible loss of recognition accuracy.

本研究提出了一个用于图像中字体识别、字体推荐、字体相似度等任务的深度学习模型 DeepFont，并使用卷积神经网络、模型压缩等技术对该模型进行优化。实验证明，该模型在我们的数据集中平均精度高于 80%，同时模型大小得到约 6 倍的压缩，而且在字体选择和建议中可产生较好的字体相似度度量。