论文标题
增强历史文档图像二进制的变异增强
Variational Augmentation for Enhancing Historical Document Image Binarization
论文作者
论文摘要
历史文档图像二进制化是图像处理中众所周知的分割问题。尽管无处不在，但传统的阈值算法在严重退化的文档图像上取得了有限的成功。随着深度学习的出现，提出了几种分割模型，这些模型在该领域取得了重大进展，但受到大型培训数据集的不可用而受到限制。为了减轻这个问题，我们提出了一个新颖的两阶段框架 - 第一个框架包括使用变量推理生成退化样品的生成器，第二个是基于CNN的二进制网络，该网络训练生成的数据。我们在一系列DIBCO数据集上评估了我们的框架，在该数据集中，它在以前的最新方法中实现了竞争结果。
Historical Document Image Binarization is a well-known segmentation problem in image processing. Despite ubiquity, traditional thresholding algorithms achieved limited success on severely degraded document images. With the advent of deep learning, several segmentation models were proposed that made significant progress in the field but were limited by the unavailability of large training datasets. To mitigate this problem, we have proposed a novel two-stage framework -- the first of which comprises a generator that generates degraded samples using variational inference and the second being a CNN-based binarization network that trains on the generated data. We evaluated our framework on a range of DIBCO datasets, where it achieved competitive results against previous state-of-the-art methods.
