A Robust Split-and-Merge Text Segmentation Approach for Images

Zhan, Yaowen; Wang, Weiqiang; Gao, Wen

doi:10.1109/icpr.2006.169

Cited by 17 publications

(4 citation statements)

References 7 publications

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“…The disadvantage is the long processing time and low efficiency. The representative algorithms include the connected-region algorithm [24], run-length smoothing algorithm [25], Voronoi diagram algorithm [26], etc. The main idea of the connected domain-based segmentation method is to first find all the connected domains in the image, and then merge them into a larger connected domain based on the intra-and inter-character and line spacing of the text [27,28].…”

Section: Traditional Layout Analysis Methodsmentioning

confidence: 99%

Research Progress on the Aesthetic Quality Assessment of Complex Layout Images Based on Deep Learning

Pu,

Liu,

Chen

et al. 2023

Applied Sciences

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With the development of the information age, the layout image is no longer a simple combination of text and graphics, but covers the complex layout image obtained from text, graphics, images and other layout elements through the process of artistic design, pre-press processing, typesetting, and so on. At present, the field of aesthetic-quality assessment mainly focuses on photographic images, and the aesthetic-quality assessment of complex layout images is rarely reported. However, the design of complex layout images such as posters, packaging labels, advertisements, etc., cannot be separated from the evaluation of aesthetic quality. In this paper, layout analysis is performed on complex layout images. Traditional and deep-learning-based methods for image layout analysis and aesthetic-quality assessment are reviewed and analyzed. Finally, the features, advantages and applications of common image aesthetic-quality assessment datasets and layout analysis datasets are compared and analyzed. Limitations and future perspectives of aesthetic assessment of complex layout images are discussed in relation to layout analysis and aesthetic characteristics.

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Section: Traditional Layout Analysis Methodsmentioning

confidence: 99%

Research Progress on the Aesthetic Quality Assessment of Complex Layout Images Based on Deep Learning

Pu,

Liu,

Chen

et al. 2023

Applied Sciences

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“…Similarly, we must define relevant segments within audio recordings to create cohesive text units (e.g., from audio show segments). For large data, automatic segmentation procedures find cohesive text or audio units, commonly splitting or combining parts of an image or audio recording into cohesive text segments (Shinde and Chougule 2012; Smith 2007; Zhan, Wang, and Gao 2006). We therefore refer to errors related to creating coherent text segments as text-to-documents errors.…”

Section: The Total Corpus Quality Frameworkmentioning

confidence: 99%

From Documents to Data: A Framework for Total Corpus Quality

Bodell

Magnusson

Mützel

2022

Socius: Sociological Research for a Dynamic World

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As large corpora of digitized text become increasingly available, researchers are rediscovering textual data’s potential fruitfulness for inquiries into social and cultural phenomena. Although textual corpora promise to enrich our knowledge of the social world, avoiding problems related to data quality remains a challenge to related empirical research. Hence, evaluating the quality of a corpus will be pivotal for future social scientific inquiries. The authors propose a conceptual framework for total corpus quality, incorporating three crucial dimensions: total corpus error, corpus comparability, and corpus reproducibility. These dimensions affect the validity and reliability of inferences drawn from textual data. In addition, the authors’ framework provides insights toward evaluating and improving studies on the basis of large-scale textual analyses. After outlining this framework, the authors then illustrate an application of the total corpus quality framework by an example case study using digitized newspaper articles to study topic salience over 75 years.

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“…Existing methods related to this problems are based on applying adaptive color reduction (ACR), Principal Component Analyzer (PCA) and Self-Organized Feature Map (SOFM) [3]. Zhan et al uses multiscale wavelet features and SVM classifier [4]. Other methods propose Globally Matched Wavelet Filters (GWM) with fisher classifiers [5] and a method insensitive to orientation, size and noise [6].…”

Section: Introductionmentioning

confidence: 99%

Gamma correction acceleration for real-time text extraction from complex colored images

Kachouri¹,

Armas²,

Akil³

2015

2015 IEEE International Conference on Image Processing (ICIP)

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International audienceText extraction from complex colored images involves the suppression of unwanted background while keeping text features. Imaging devices are almost omnipresent and the unrestricted conditions of the images present new challenges for real-time OCR systems. The recently proposed Gamma Correction Method [1] is a robust and good quality method for text extraction in complex colored images. However it requires a large amount of computing resources and is not well suited for real-time applications. In this paper we propose an efficient acceleration of the GCM to drastically reduce its execution-time, while preserving the text extraction quality. Experimental results on ICDAR dataset show that our approach is effective and can reach a speedup of up to 11.430

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A Robust Split-and-Merge Text Segmentation Approach for Images

Cited by 17 publications

References 7 publications

Research Progress on the Aesthetic Quality Assessment of Complex Layout Images Based on Deep Learning

Research Progress on the Aesthetic Quality Assessment of Complex Layout Images Based on Deep Learning

From Documents to Data: A Framework for Total Corpus Quality

Gamma correction acceleration for real-time text extraction from complex colored images

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