A comprehensive survey of mostly textual document segmentation algorithms since 2008
TLDR
This survey highlights the variety of the approaches that have been proposed for document image segmentation since 2008 and provides a clear typology of documents and of document images segmentation algorithms.About:
This article is published in Pattern Recognition.The article was published on 2017-04-01 and is currently open access. It has received 84 citations till now. The article focuses on the topics: Document clustering & Image segmentation.read more
Figures
Table 1: Summary of the characteristics of the main document segmentation algorithms. The type of input is the kind of document that can be processed. Multi-layered indicates whether it can process documents with overlapping content. 
Figure 8: From top to bottom: evolution of the average number of different languages, the average number of different documents and the average data set size (scale on the right). The dotted lines show the linear tendency of these values. 
Figure 7: Breakdown of the number of algorithms based on the number of languages and document types in the evaluation corpus. The radius of each bubble is proportional to the number of algorithms which is also the vertical coordinate. The color of the bubbles relates to the size of the corpus. 
Figure 1: A classical document content extraction process 
Figure 4: Comparison of thresholding and error diffusion binarization techniques. Left: with thresholding, right: with error diffusion. 
Figure 6: Trends of algorithm techniques and evaluations between 2008 and 2015
Citations
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Journal ArticleDOI
Recent advances in convolutional neural networks
Jiuxiang Gu,Zhenhua Wang,Jason Kuen,Lianyang Ma,Amir Shahroudy,Bing Shuai,Ting Liu,Xingxing Wang,Gang Wang,Jianfei Cai,Tsuhan Chen +10 more
TL;DR: A broad survey of the recent advances in convolutional neural networks can be found in this article, where the authors discuss the improvements of CNN on different aspects, namely, layer design, activation function, loss function, regularization, optimization and fast computation.
Posted Content
Recent Advances in Convolutional Neural Networks
Jiuxiang Gu,Zhenhua Wang,Jason Kuen,Lianyang Ma,Amir Shahroudy,Bing Shuai,Ting Liu,Xingxing Wang,Li Wang,Gang Wang,Jianfei Cai,Tsuhan Chen +11 more
TL;DR: This paper details the improvements of CNN on different aspects, including layer design, activation function, loss function, regularization, optimization and fast computation, and introduces various applications of convolutional neural networks in computer vision, speech and natural language processing.
Proceedings ArticleDOI
FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents
TL;DR: This work presents a new dataset for form understanding in noisy scanned documents (FUNSD) that aims at extracting and structuring the textual content of forms, and is the first publicly available dataset with comprehensive annotations to address FoUn task.
Journal ArticleDOI
A Two-Stage Method for Text Line Detection in Historical Documents
TL;DR: In this paper, a two-stage text line detection method for historical documents is presented, where the first stage labels pixels to belong to one of the three classes: baseline, separator and other, and the second stage performs bottom-up clustering to build baselines.
Journal ArticleDOI
Document Layout Analysis: A Comprehensive Survey
TL;DR: This survey paper presents a critical study of different document layout analysis techniques and discusses comprehensively the different phases of the DLA algorithms based on a general framework that is formed as an outcome of reviewing the research in the field.
References
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