Few-Shot Pixel-Precise Document Layout Segmentation via Dynamic Instance Generation and Local Thresholding.

IF 6.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Neural Systems Pub Date : 2023-10-01 Epub Date: 2023-08-10 DOI:10.1142/S0129065723500521
Axel De Nardin, Silvia Zottin, Claudio Piciarelli, Emanuela Colombi, Gian Luca Foresti
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Abstract

Over the years, the humanities community has increasingly requested the creation of artificial intelligence frameworks to help the study of cultural heritage. Document Layout segmentation, which aims at identifying the different structural components of a document page, is a particularly interesting task connected to this trend, specifically when it comes to handwritten texts. While there are many effective approaches to this problem, they all rely on large amounts of data for the training of the underlying models, which is rarely possible in a real-world scenario, as the process of producing the ground truth segmentation task with the required precision to the pixel level is a very time-consuming task and often requires a certain degree of domain knowledge regarding the documents at hand. For this reason, in this paper, we propose an effective few-shot learning framework for document layout segmentation relying on two novel components, namely a dynamic instance generation and a segmentation refinement module. This approach is able of achieving performances comparable to the current state of the art on the popular Diva-HisDB dataset, while relying on just a fraction of the available data.

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通过动态实例生成和局部阈值实现少镜头像素精确文档布局分割。
多年来,人文社会越来越多地要求创建人工智能框架,以帮助研究文化遗产。文档布局分割旨在识别文档页面的不同结构组件,这是一项与这一趋势相关的特别有趣的任务,尤其是在手写文本方面。虽然有很多有效的方法来解决这个问题,但它们都依赖于大量的数据来训练底层模型,这在现实世界中是不可能的,由于产生具有所需像素级精度的地面实况分割任务的过程是非常耗时的任务,并且通常需要关于手头文档的一定程度的领域知识。因此,在本文中,我们提出了一个有效的文档布局分割的少镜头学习框架,该框架依赖于两个新的组件,即动态实例生成和分割细化模块。这种方法能够在流行的Diva HisDB数据集上实现与当前技术水平相当的性能,同时只依赖于可用数据的一小部分。
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来源期刊
International Journal of Neural Systems
International Journal of Neural Systems 工程技术-计算机:人工智能
CiteScore
11.30
自引率
28.80%
发文量
116
审稿时长
24 months
期刊介绍: The International Journal of Neural Systems is a monthly, rigorously peer-reviewed transdisciplinary journal focusing on information processing in both natural and artificial neural systems. Special interests include machine learning, computational neuroscience and neurology. The journal prioritizes innovative, high-impact articles spanning multiple fields, including neurosciences and computer science and engineering. It adopts an open-minded approach to this multidisciplinary field, serving as a platform for novel ideas and enhanced understanding of collective and cooperative phenomena in computationally capable systems.
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