Fully Convolutional Neural Networks for Newspaper Article Segmentation

Meier, Benjamin; Stadelmann, Thilo; Stampfli, Jan; Arnold, Marek; Cieliebak, Mark

doi:10.1109/icdar.2017.75

Cited by 34 publications

(11 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This indicates manual segmentation, which involves much less effort than OCR postcorrection, is a worthy target when some manual annotation resources are available. Arguably, segmentation can also be improved further by the inclusion of visual features (Meier et al, 2017), which appears a promising direction for future research.…”

Section: Discussionmentioning

confidence: 99%

“…The task tackled in this paper can be split into two sub-tasks: the detection of the different articles and the clustering of parts of the same article. Most previous work performs the segmentation of newspaper pages directly at the image level (Hebert et al, 2014;Meier et al, 2017). This strategy avoids having to deal with spelling errors arising from OCR.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Clustering-Based Article Identification in Historical Newspapers

Riedl

Betz²,

Padó

2019

Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities An

View full text Add to dashboard Cite

This article focuses on the problem of identifying articles and recovering their text from within and across newspaper pages when OCR just delivers one text file per page. We frame the task as a segmentation plus clustering step. Our results on a sample of 1912 New York Tribune magazine shows that performing the clustering based on similarities computed with word embeddings outperforms a similarity measure based on character n-grams and words. Furthermore, the automatic segmentation based on the text results in low scores, due to the low quality of some OCRed documents.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Clustering-Based Article Identification in Historical Newspapers

Riedl

Betz²,

Padó

2019

Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities An

View full text Add to dashboard Cite

show abstract

“…The fully convolutional neural network used in our second approach is built with three logical parts (cp. Meier et al (2017) and Figure 8). Initially, feature extraction is done the same way as with a standard CNN.…”

Section: Cnn-based Pixel Classification Vs One-pass Fcnsmentioning

confidence: 94%

Beyond ImageNet: Deep Learning in Industrial Practice

Stadelmann

Tolkachev

Sick

et al. 2019

Applied Data Science

Self Cite

View full text Add to dashboard Cite

Deep learning (DL) methods have gained considerable attention since 2014. In this chapter we briefly review the state of the art in DL and then give several examples of applications from diverse areas of application. We will focus on convolutional neural networks (CNNs), which have since the seminal work of Krizhevsky et al. (2012) revolutionized image classification and even started surpassing human performance on some benchmark data sets (Ciresan et al., 2012a, He et al., 2015a). While deep neural networks have become popular primarily for image classification tasks, they can also be successfully applied to other areas and problems with some local structure in the data. We will first present a classical application of CNNs on image-like data, in particular, phenotype classification of cells based on their morphology, and then extend the task to clustering voices based on their spectrograms. Next, we will describe DL applications to semantic segmentation of newspaper pages into their corresponding articles based on clues in the pixels, and outlier detection in a predictive maintenance setting. We conclude by giving advice on how to work with DL having limited resources (e.g., training data).

show abstract

“…Only a few recent work attempt to make use of image and/or localized, two-dimension text information. Meier et al [2017] use a FCN based on image and OCR output information in order to detect articles in newspaper images (no further segment types). In this case text is reduced to a binary feature information (a pixel has text or not) and the lexical and semantic dimensions are not taken into account.…”

Section: Related Workmentioning

confidence: 99%

Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers

Barman¹,

Ehrmann²,

Clematide³

et al. 2021

Journal of Data Mining &Amp; Digital Humanities

View full text Add to dashboard Cite

The massive amounts of digitized historical documents acquired over the last decades naturally lend themselves to automatic processing and exploration. Research work seeking to automatically process facsimiles and extract information thereby are multiplying with, as a first essential step, document layout analysis. If the identification and categorization of segments of interest in document images have seen significant progress over the last years thanks to deep learning techniques, many challenges remain with, among others, the use of finer-grained segmentation typologies and the consideration of complex, heterogeneous documents such as historical newspapers. Besides, most approaches consider visual features only, ignoring textual signal. In this context, we introduce a multimodal approach for the semantic segmentation of historical newspapers that combines visual and textual features. Based on a series of experiments on diachronic Swiss and Luxembourgish newspapers, we investigate, among others, the predictive power of visual and textual features and their capacity to generalize across time and sources. Results show consistent improvement of multimodal models in comparison to a strong visual baseline, as well as better robustness to high material variance.

show abstract

Fully Convolutional Neural Networks for Newspaper Article Segmentation

Cited by 34 publications

References 13 publications

Clustering-Based Article Identification in Historical Newspapers

Clustering-Based Article Identification in Historical Newspapers

Beyond ImageNet: Deep Learning in Industrial Practice

Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers

Contact Info

Product

Resources

About