Text -image separation in Devanagari documents

Khedekar, Swapnil; Ramanaprasad, V.; Setlur, Srirangaraj; Govindaraju, Venu

doi:10.1109/icdar.2003.1227861

Cited by 31 publications

(17 citation statements)

References 6 publications

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“…RLSA [44], XY-CUT [17], etc.). However, such algorithms rely on a priori knowledge in order to properly segment and characterize the document image content.…”

Section: Introductionmentioning

confidence: 99%

Texture feature evaluation for segmentation of historical document images

Mehri

Gomez‐Krämer

Héroux

et al. 2013

Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing

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International audienceTexture feature analysis has undergone tremendous growth in recent years. It plays an important role for the analysis of many kinds of images. More recently, the use of texture analysis techniques for historical document image segmen-tation has become a logical and relevant choice in the conditions of significant document image degradation and in the context of lacking information on the document structure such as the document model and the typographical parameters. However, previous work in the use of texture analysis for segmentation of digitized historical document images has been limited to separately test one of the well-known texture-based approaches such as autocorrelation function, Grey Level Co-occurrence Matrix (GLCM), Gabor filters, gradient, wavelets, etc. In this paper we raise the question of which texture-based method could be better suited for discriminating on the one hand graphical regions from textual ones and on the other hand for separating textual regions with different sizes and fonts. The objective of this paper is to compare some of the well-known texture-based approaches: autocorrelation function, GLCM, and Gabor filters , used in a segmentation of digitized historical document images. Texture features are briefly described and quantitative results are obtained on simplified historical document images. The achieved results are very encouraging

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“…RLSA [44], XY-CUT [17], etc.). However, such algorithms rely on a priori knowledge in order to properly segment and characterize the document image content.…”

Section: Introductionmentioning

confidence: 99%

Texture feature evaluation for segmentation of historical document images

Mehri

Gomez‐Krämer

Héroux

et al. 2013

Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing

View full text Add to dashboard Cite

show abstract

“…Commonly used approaches to determine text height estimation are based on Projection Profiles [14], [15], [16], XY-CUT algorithm [17], and Run Length Smearing Algorithm (RLSA) [18]. They are all based on different ways to directly integrate the original image along rows, columns or, rarely, diagonal directions.…”

Section: Related Workmentioning

confidence: 99%

ATHENA: Automatic text height extraction for the analysis of old handwritten manuscripts

Pintus

Yang

Rushmeier

2013

2013 Digital Heritage International Congress (DigitalHeritage)

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A massive digital acquisition of huge sets of deteriorating historical documents is mandatory due to their value and delicacy. The study and the browsing of such digital libraries is becoming crucial for scholars in the Cultural Heritage field, but it requires automatic tools for analyzing and indexing those dataset items. We present here a layout analysis method to perform automatic text height estimation, without the need of any kind of manual intervention and user defined parameters. It proves to be a robust technique in the case of very noisy and damaged handwritten manuscripts. The effectiveness of the method is demonstrated on a huge heterogeneous corpus of medieval manuscripts, with different writing styles, and affected by other uncontrollable factors, such as ink bleed-through, background noise, and overtyping text lines.

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“…Sometimes, detected text areas do not contain text but many small elements as high as text ( figure 7). Thus, a text/graphic separation method [11] is applied to remove areas without text. This method compares vertical and horizontal projected histogram of each text area.…”

Section: Filteringmentioning

confidence: 99%

Robust Frame and Text Extraction from Comic Books

Rigaud

Tsopzé

Burie

et al. 2013

Graphics Recognition. New Trends and Challenges

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Abstract. Comic books constitute an important heritage in many countries. Nowadays, digitization allows to search directly from content instead of metadata only (e.g. album title or author name). Few studies have been done in this direction. Only frame and speech balloon extraction have been experimented in the case of simple page structure. In fact, the page structure depends on the author which is why many different structures and drawings exist. Despite the differences, drawings have a common characteristic because of design process: they are all surrounded by a black line. In this paper, we propose to rely on this particularity of comic books to automatically extract frame and text using a connectedcomponent labeling analysis. The approach is compared with some existing methods found in the literature and results are presented.

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Text -image separation in Devanagari documents

Cited by 31 publications

References 6 publications

Texture feature evaluation for segmentation of historical document images

Texture feature evaluation for segmentation of historical document images

ATHENA: Automatic text height extraction for the analysis of old handwritten manuscripts

Robust Frame and Text Extraction from Comic Books

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