Open Access   Article Go Back

Video Text Detection and Recognition Based on a Transferred Deep Convolutional Neural Network

Namrata Choudhary1 , Kirti Jain2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-5 , Page no. 718-723, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i5.718723

Online published on May 31, 2019

Copyright © Namrata Choudhary, Kirti Jain . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Namrata Choudhary, Kirti Jain, “Video Text Detection and Recognition Based on a Transferred Deep Convolutional Neural Network,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.718-723, 2019.

MLA Style Citation: Namrata Choudhary, Kirti Jain "Video Text Detection and Recognition Based on a Transferred Deep Convolutional Neural Network." International Journal of Computer Sciences and Engineering 7.5 (2019): 718-723.

APA Style Citation: Namrata Choudhary, Kirti Jain, (2019). Video Text Detection and Recognition Based on a Transferred Deep Convolutional Neural Network. International Journal of Computer Sciences and Engineering, 7(5), 718-723.

BibTex Style Citation:
@article{Choudhary_2019,
author = {Namrata Choudhary, Kirti Jain},
title = {Video Text Detection and Recognition Based on a Transferred Deep Convolutional Neural Network},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {7},
Issue = {5},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {718-723},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4305},
doi = {https://doi.org/10.26438/ijcse/v7i5.718723}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i5.718723}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4305
TI - Video Text Detection and Recognition Based on a Transferred Deep Convolutional Neural Network
T2 - International Journal of Computer Sciences and Engineering
AU - Namrata Choudhary, Kirti Jain
PY - 2019
DA - 2019/05/31
PB - IJCSE, Indore, INDIA
SP - 718-723
IS - 5
VL - 7
SN - 2347-2693
ER -

VIEWS PDF XML
249 157 downloads 115 downloads
  
  
           

Abstract

The text presented in videos contains important information for content analysis, indexing, and retrieval of videos. The key technique for extracting this information is to find, verify, and recognize video text in various languages and fonts against complex backgrounds. In this paper, we propose a novel method that transferred deep convolutional neural networks for detecting and recognizing video text. We partition the candidate text regions into candidate text lines by projection analysis using two alternative methods. We develop a novel fuzzy c-means clustering-based separation algorithm to obtain a clean text layer from complex backgrounds so that the text is correctly recognized by commercial optical character recognition software. The proposed method is robust and has good performance on video text detection and recognition, which was evaluated on three publicly available test data sets and on the high-resolution test data set we constructed.

Key-Words / Index Term

Video Text Detection, Recognition, Transferred convolutional neural network, Fuzzy c-means Clustering.

References

[1] Y. A. Aslandogan and C. T. Yu, ‘‘Techniques and systems for image and video retrieval,’’ IEEE Trans. Knowl. Data Eng., vol. 11, no. 1, pp. 56–63, Jan. 1999.
[2] H. Bhaskar and L. Mihaylova, ‘‘Combined feature-level video indexing using block-based motion estimation,’’ in Proc. Conf. Inf. Fusion, Jul. 2010, pp. 1–8.
[3] W. Hu, N. Xie, L. Li, X. Zeng, and S. Maybank, ‘‘A survey on visual content-based video indexing and retrieval,’’ IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 41, no. 6, pp. 797–819, Nov. 2011.
[4] K. Jung, K. I. Kim, and A. K. Jain, ‘‘Text information extraction in images and video: A survey,’’ Pattern Recognit., vol. 37, no. 5, pp. 977–997, 2004.
[5] M. Khodadadi and A. Behrad, ‘‘Text localization, extraction and inpainting in color images,’’ in Proc. Iranian Conf. Elect. Eng., May 2012, pp. 1035–1040.
[6] A. Mosleh, N. Bouguila, and A. B. Hamza, ‘‘Automatic inpainting scheme for video text detection and removal,’’ IEEE Trans. Image Process., vol. 22, no. 11, pp. 4460–4472, Nov. 2013.
[7] M. Cai, J. Song, and M. R. Lyu, ‘‘A new approach for video text detection,’’ in Proc. Int. Conf. Image Process., vol. 1, Sep. 2002, pp. I-117–I-120.
[8] T. Yusufu, Y. Wang, and X. Fang, ‘‘A video text detection and tracking system,’’ in Proc. IEEE Int. Symp. Multimedia, Dec. 2013, pp. 522–529.
[9] X. Huang, ‘‘A novel video text extraction approach based on Log–Gabor filters,’’ in Proc. Int. Congr. Image Signal Process., vol. 1, Oct. 2011, pp. 474–478.
[10] P. Shivakumara, W. Huang, and C. L. Tan, ‘‘Efficient video text detection using edge features,’’ in Proc. Int. Conf. Pattern Recognit., Dec. 2008, pp. 1–4.
[11] X. Zhao, K.-H. Lin, Y. Fu, Y. Hu, Y. Liu, and T. S. Huang, ‘‘Text from corners: A novel approach to detect text and caption in videos,’’ IEEE Image Process., vol. 20, no. 3, pp. 790–799, Mar. 2011.