论文标题
深层生成模型的性能评估，用于生成手写字符图像
Performance Evaluation of Deep Generative Models for Generating Hand-Written Character Images
论文作者
论文摘要
文献中有许多关于生成各种图像的工作，例如手写字符（MNIST数据集），场景图像（CIFAR-10数据集），各种对象图像（Imagenet数据集），路签板图像（SVHN数据集）等。不幸的是，在文档图像处理的域中，工作量非常有限。自动图像生成可以仅借助有限的标记数据，从而导致标记数据集的大量增加。各种深层生成模型可以主要分为两类。第一类是自动编码器（AE），第二类是生成对抗网络（GAN）。在本文中，我们评估了各种AE和gans，并比较了他们在手写数字数据集（MNIST）以及印度尼西亚巴厘岛语言的历史手写字符数据集上的表现。此外，通过使用字符识别工具来计算这些生成的字符相对于原始字符图像的统计性能来识别这些生成的字符。
There have been many work in the literature on generation of various kinds of images such as Hand-Written characters (MNIST dataset), scene images (CIFAR-10 dataset), various objects images (ImageNet dataset), road signboard images (SVHN dataset) etc. Unfortunately, there have been very limited amount of work done in the domain of document image processing. Automatic image generation can lead to the enormous increase of labeled datasets with the help of only limited amount of labeled data. Various kinds of Deep generative models can be primarily divided into two categories. First category is auto-encoder (AE) and the second one is Generative Adversarial Networks (GANs). In this paper, we have evaluated various kinds of AE as well as GANs and have compared their performances on hand-written digits dataset (MNIST) and also on historical hand-written character dataset of Indonesian BALI language. Moreover, these generated characters are recognized by using character recognition tool for calculating the statistical performance of these generated characters with respect to original character images.
