Standard
Localization of Text in Photorealistic Images. / Grishkin, Valery ; Ebral, Alexander; Iakushkin, Oleg .
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Conference proceedings. ed. / Sanjay Misra; Osvaldo Gervasi; Beniamino Murgante; Elena Stankova; Vladimir Korkhov; Carmelo Torre; Eufemia Tarantino; Ana Maria A.C. Rocha; David Taniar; Bernady O. Apduhan. Springer Nature, 2019. p. 825-834 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11622 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Harvard
Grishkin, V, Ebral, A & Iakushkin, O 2019,
Localization of Text in Photorealistic Images. in S Misra, O Gervasi, B Murgante, E Stankova, V Korkhov, C Torre, E Tarantino, AMAC Rocha, D Taniar & BO Apduhan (eds),
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Conference proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11622 LNCS, Springer Nature, pp. 825-834, 19th International Conference on Computational Science and Its Applications, ICCSA 2019, Saint Petersburg, Russian Federation,
1/07/19.
https://doi.org/10.1007/978-3-030-24305-0_63
APA
Grishkin, V., Ebral, A., & Iakushkin, O. (2019).
Localization of Text in Photorealistic Images. In S. Misra, O. Gervasi, B. Murgante, E. Stankova, V. Korkhov, C. Torre, E. Tarantino, A. M. A. C. Rocha, D. Taniar, & B. O. Apduhan (Eds.),
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Conference proceedings (pp. 825-834). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11622 LNCS). Springer Nature.
https://doi.org/10.1007/978-3-030-24305-0_63
Vancouver
Grishkin V, Ebral A, Iakushkin O.
Localization of Text in Photorealistic Images. In Misra S, Gervasi O, Murgante B, Stankova E, Korkhov V, Torre C, Tarantino E, Rocha AMAC, Taniar D, Apduhan BO, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Conference proceedings. Springer Nature. 2019. p. 825-834. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
https://doi.org/10.1007/978-3-030-24305-0_63
Author
Grishkin, Valery ; Ebral, Alexander ; Iakushkin, Oleg . /
Localization of Text in Photorealistic Images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Conference proceedings. editor / Sanjay Misra ; Osvaldo Gervasi ; Beniamino Murgante ; Elena Stankova ; Vladimir Korkhov ; Carmelo Torre ; Eufemia Tarantino ; Ana Maria A.C. Rocha ; David Taniar ; Bernady O. Apduhan. Springer Nature, 2019. pp. 825-834 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
BibTeX
@inproceedings{5bf984879eac4fcc97fb1011547c3340,
title = "Localization of Text in Photorealistic Images",
abstract = "Detection and localization of text in photorealistic images is a difficult, and not yet completely solved, problem. We propose the approach to solving this problem based on the method of semantic image segmentation. In this interpretation, text characters are treated as objects to be segmented. In this paper proposes the network architecture for text localization, describes the procedure for the formation of the training set, and considers the algorithm for pre-processing images, reducing the amount of processed data and simplifying the segmentation of the object “background”. The network architecture is a modification of well-known DeepLabv3 network and takes into account the specifics of images of text characters. The proposed method is able to determine the location of text characters in the images with acceptable accuracy. Experimental results of assessing the quality of text localization by the IoU criterion (Intersection over Union) showed that the obtained accuracy is sufficient for further text recognition.",
keywords = "Convolution neural network, Semantic segmentation, Text localization",
author = "Valery Grishkin and Alexander Ebral and Oleg Iakushkin",
year = "2019",
doi = "10.1007/978-3-030-24305-0_63",
language = "English",
isbn = "9783030243043",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "825--834",
editor = "Sanjay Misra and Osvaldo Gervasi and Beniamino Murgante and Elena Stankova and Vladimir Korkhov and Carmelo Torre and Eufemia Tarantino and Rocha, {Ana Maria A.C.} and David Taniar and Apduhan, {Bernady O.}",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",
note = "19th International Conference on Computational Science and Its Applications, ICCSA 2019 ; Conference date: 01-07-2019 Through 04-07-2019",
}
RIS
TY - GEN
T1 - Localization of Text in Photorealistic Images
AU - Grishkin, Valery
AU - Ebral, Alexander
AU - Iakushkin, Oleg
N1 - Conference code: 19
PY - 2019
Y1 - 2019
N2 - Detection and localization of text in photorealistic images is a difficult, and not yet completely solved, problem. We propose the approach to solving this problem based on the method of semantic image segmentation. In this interpretation, text characters are treated as objects to be segmented. In this paper proposes the network architecture for text localization, describes the procedure for the formation of the training set, and considers the algorithm for pre-processing images, reducing the amount of processed data and simplifying the segmentation of the object “background”. The network architecture is a modification of well-known DeepLabv3 network and takes into account the specifics of images of text characters. The proposed method is able to determine the location of text characters in the images with acceptable accuracy. Experimental results of assessing the quality of text localization by the IoU criterion (Intersection over Union) showed that the obtained accuracy is sufficient for further text recognition.
AB - Detection and localization of text in photorealistic images is a difficult, and not yet completely solved, problem. We propose the approach to solving this problem based on the method of semantic image segmentation. In this interpretation, text characters are treated as objects to be segmented. In this paper proposes the network architecture for text localization, describes the procedure for the formation of the training set, and considers the algorithm for pre-processing images, reducing the amount of processed data and simplifying the segmentation of the object “background”. The network architecture is a modification of well-known DeepLabv3 network and takes into account the specifics of images of text characters. The proposed method is able to determine the location of text characters in the images with acceptable accuracy. Experimental results of assessing the quality of text localization by the IoU criterion (Intersection over Union) showed that the obtained accuracy is sufficient for further text recognition.
KW - Convolution neural network
KW - Semantic segmentation
KW - Text localization
UR - http://www.scopus.com/inward/record.url?scp=85068593365&partnerID=8YFLogxK
UR - http://www.mendeley.com/research/localization-text-photorealistic-images
U2 - 10.1007/978-3-030-24305-0_63
DO - 10.1007/978-3-030-24305-0_63
M3 - Conference contribution
AN - SCOPUS:85068593365
SN - 9783030243043
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 825
EP - 834
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Misra, Sanjay
A2 - Gervasi, Osvaldo
A2 - Murgante, Beniamino
A2 - Stankova, Elena
A2 - Korkhov, Vladimir
A2 - Torre, Carmelo
A2 - Tarantino, Eufemia
A2 - Rocha, Ana Maria A.C.
A2 - Taniar, David
A2 - Apduhan, Bernady O.
PB - Springer Nature
T2 - 19th International Conference on Computational Science and Its Applications, ICCSA 2019
Y2 - 1 July 2019 through 4 July 2019
ER -
