Abstract
We present a generative document-specific approach to character analysis and recognition in text lines. Our main idea is to build on unsupervised multi-object segmentation methods and in particular those that reconstruct images based on a limited amount of visual elements, called sprites. Our approach can learn a large number of different characters and leverage line-level annotations when available. Our contribution is twofold. First, we provide the first adaptation and evaluation of a deep unsupervised multi-object segmentation approach for text line analysis. Since these methods have mainly been evaluated on synthetic data in a completely unsupervised setting, demonstrating that they can be adapted and quantitatively evaluated on real text images and that they can be trained using weak supervision are significant progresses. Second, we demonstrate the potential of our method for new applications, more specifically in the field of paleography, which studies the history and variations of handwriting, and for cipher analysis. We evaluate our approach on three very different datasets: a printed volume of the Google1000 dataset, the Copiale cipher and historical handwritten charters from the 12th and early 13th century.
Abstract (translated)
我们提出了一种针对文本行中字符分析和识别的生成式特定方法。我们的主要思想是在无监督的多对象分割方法的基础上建立,特别是那些基于少量视觉元素的Sprites模型的重建图像的方法。我们的方法可以在可用时学习大量不同字符,并利用水平注释。我们的贡献有两个。首先,我们提供了对深度无监督多对象分割方法的适应和评估,因为这些方法主要基于完全无监督的环境评估合成数据,证明了这些方法可以在真实文本图像上适应和定量评估,并使用弱监督进行训练,这是一个重要的进展。其次,我们展示了我们方法对新应用的潜力,特别是训读学,研究手写文字的历史和变异,以及密码学分析。我们评估了三个非常不同的数据集:Google1000数据的印刷版, Copiale密码和12世纪和13世纪初的手写文件。
URL
https://arxiv.org/abs/2302.01660
