Abstract
In this paper, we propose a Seed-Augment-Train/Transfer (SAT) framework that contains a synthetic seed image dataset generation procedure for languages with different numeral systems using freely available open font file datasets. This seed dataset of images is then augmented to create a purely synthetic training dataset, which is in turn used to train a deep neural network and test on held-out real world handwritten digits dataset spanning five Indic scripts, Kannada, Tamil, Gujarati, Malayalam, and Devanagari. We showcase the efficacy of this approach both qualitatively, by training a Boundary-seeking GAN (BGAN) that generates realistic digit images in the five languages, and also quantitatively by testing a CNN trained on the synthetic data on the real-world datasets. This establishes not only an interesting nexus between the font-datasets-world and transfer learning but also provides a recipe for universal-digit classification in any script.
Abstract (translated)
在本文中，我们提出了一个种子增强训练/传输（SAT）框架，该框架包含一个合成种子图像数据集生成过程，用于使用自由可用的开放字体文件数据集生成具有不同数字系统的语言。这种图像种子数据集随后被扩充以创建一个纯粹的合成训练数据集，该数据集反过来又被用来训练一个深层神经网络，并测试跨越五个印度语脚本、卡纳达语、泰米尔语、古吉拉特语、马拉雅拉姆语和天成文书（devanagari）的手写数字数据集。我们通过培训一个边界搜索gan（bgan），在五种语言中生成真实数字图像，并通过测试一个CNN对真实数据集上的合成数据进行培训，从质量上展示了这种方法的有效性。这不仅在字体数据集世界和传输学习之间建立了一个有趣的联系，而且为任何脚本中的通用数字分类提供了一个方法。
URL
https://arxiv.org/abs/1905.08633
