<abstract xmlns="http://www.w3.org/1999/xhtml"><p>There are a large number of historical documents in libraries and other archives throughout the world.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 <italic>ICDAR 2009</italic> competition.</p></abstract>

There are a large number of historical documents in libraries and other archives throughout the world.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.

Line Segmentation of Handwritten Text Using Histograms and Tensor Voting

Wrocław University of Science and Technology

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Line Segmentation of Handwritten Text Using Histograms and Tensor Voting"}

There are a large number of historical documents in libraries and other archives throughout the world.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...

Line Segmentation of Handwritten Text Using Histograms and Tensor Voting

Online veröffentlicht: 29. Sept. 2020

Seitenbereich: 585 - 596

Eingereicht: 24. Dez. 2019

Akzeptiert: 02. Juli 2020

DOI: https://doi.org/10.34768/amcs-2020-0043

Schlüsselwörter
document image processing, handwritten text line segmentation, projection profile, text string, off-line cursive script recognition, competition

© 2020 Tomasz Babczyński et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Line Segmentation of Handwritten Text Using Histograms and Tensor Voting

Online veröffentlicht: 29. Sept. 2020

Seitenbereich: 585 - 596

Eingereicht: 24. Dez. 2019

Akzeptiert: 02. Juli 2020

DOI: https://doi.org/10.34768/amcs-2020-0043

Schlüsselwörterdocument image processing, handwritten text line segmentation, projection profile, text string, off-line cursive script recognition, competition

© 2020 Tomasz Babczyński et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
document image processing, handwritten text line segmentation, projection profile, text string, off-line cursive script recognition, competition