Robust method for the text line detection and splitting of overlapping text in the Latin manuscripts

Pach, Jakub; Bilski, Piotr

doi:10.22630/mgv.2014.23.3.2

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…The Hough transform based methods can cope with documents with variations in the skew between lines (Likforman-Sulem et al, 1995;Pu and Shi, 1999). Pach and Bilski (2014) proposed a robust method for the text line segmentation of medieval overlapping text. In this procedure, bounding boxes of the connected components of foreground pixels are divided into smaller parts.…”

Section: Related Workmentioning

confidence: 99%

Line segmentation of handwritten text using histograms and tensor voting

Babczyński¹,

Ptak²

2020

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There are a large number of historical documents in libraries and other archives throughout the world. Most of them are written by hand. In many cases they exist in only one specimen and are hard to reach. Digitization of such artifacts can make them available to the community. But even digitized, they remain unsearchable, and an important task is to draw the contents in the computer readable form. One of the first steps in this direction is to recognize where the lines of the text are. Computational intelligence algorithms can be used to solve this problem. In the present paper, two groups of algorithms, namely, projection-based and tensor voting-based, are compared. The performance is evaluated on a data set and with the procedure proposed by the organizers of the ICDAR 2009 competition.

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Section: Related Workmentioning

confidence: 99%

Line segmentation of handwritten text using histograms and tensor voting

Babczyński¹,

Ptak²

2020

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Text area detection in handwritten documents scanned for further processing

Pach

Antoniuk

Krupa

2020

MG&V

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In this paper we present an approach to text area detection using binary images, Constrained Run Length Algorithm and other noise reduction methods of removing the artefacts. Text processing includes various activities, most of which are related to preparing input data for further operations in the best possible way, that will not hinder the OCR algorithms. This is especially the case when handwritten manuscripts are considered, and even more so with very old documents. We present our methodology for text area detection problem, which is capable of removing most of irrelevant objects, including elements such as page edges, stains, folds etc. At the same time the presented method can handle multi-column texts or varying line thickness. The generated mask can accurately mark the actual text area, so that the output image can be easily used in further text processing steps.

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Robust method for the text line detection and splitting of overlapping text in the Latin manuscripts

Cited by 2 publications

References 13 publications

Line segmentation of handwritten text using histograms and tensor voting

Line segmentation of handwritten text using histograms and tensor voting

Text area detection in handwritten documents scanned for further processing

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