ICDAR 2015 competition on text line detection in historical documents

Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

Volumn 2015-November, Issue , 2015, Pages 1171-1175

  Murdock, Michael ^a   Reid, Shawn ^a   Hamilton, Blaine ^a   Reese, Jackson ^a  

^a Ancestry   (United States)

Author keywords

ANDAR; historical documents; origin point location detection; text line detection; text line detection competition

Indexed keywords

HISTORY; LOCATION;

ANDAR; DOCUMENT ANALYSIS; HISTORICAL DOCUMENTS; POINT LOCATION; TEXT LINE DETECTION; TEXT LINES;

CHARACTER RECOGNITION;

EID: 84962492769     PISSN: 15205363     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDAR.2015.7333945     Document Type: Conference Paper

References (17)

1. http://collections.ancestry.com/DART-2015-TextLines
2. D. Erhan, C. Szegedy, A. Toshev, D. Anguelov, "Scalable Object Detection using Deep Neural Networks" (CVPR 20(4).
3. A. Graves, S. Fernandez, and J. Schmidhuber, "Multi-dimensional recurrent neural networks," in 17th International Conference on Artificial Neural Networks, 2007, pp. 549-558.
4. K. Zuiderveld, "Contrast limited adaptive histogram equalization", in Graphics gems IV, 1994, pp. 474-487.
5. D.S. Bloomberg, G.E. Kopec, L. Dasari, "Measuring document image skew and orientation", SPIE 2422, Document Recognition II, 1995
6. A. Vinciarelli, J. Luettin, "A new normalisation technique for cursive handwritten words", Pattern Recognition Letters Vo122, 2001, pp. 1043-1050.
7. Z. Shi, S. Setlur &V. Govindaraju, "A Steerable Directional Local Profile Technique for Extraction of Handwritten Arabic Text Lines", ICDAR, 2009, pp. 176-180
8. Ntirogiannis, Konstantinos, Basilis Gatos, and Ioannis Pratikakis, "A combined approach for the binarization of handwritten document images," Pattern Recognition Letters, vol. 35, pp. 3-15, January 2014
9. J. W. Ryu, H. J. Koo, and N. I. Cho, "Language-independent text-line extraction algorithm for handwritten documents," IEEE Signal Processing Letters, vol. 21, pp. 1115-1119, September 2014
10. K. Chen, M. Seuret, M. Liwicki, J. Hennebert, and R. Ingold, "Page Segmentation of Historical Document Images with Convolutional Autoencoders." The International Conference on Document Analysis and Recognition, 2015, submitted.
11. G. E. Hinton, R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks." Science, 313, no. 5786, pp. 504-507, 2006.
12. Y. LeCun, B. L'eon, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition." in Proceedings of the IEEE, 86, no. 1I, pp. 2278-2324, 1998.
13. M. R. Lyu, J. Song, and M. Cai, "A comprehensive method for multilingual video text detection, localization, and extraction," Circuits and Systems for Video Technology, IEEE Transactions on, no. 2, pp. 243-255,2005.
14. L. Likforman-Sulem, A. Hanimyan, and C. Faure, "A hough based algorithm for extracting text lines in handwritten documents," Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on, vol. 2, pp. 774-777,1995.
15. H. J. Koo and N. J. Cho, "State estimation in a document image and its application in text block identification and text line extraction," in ECCV, Sep 2010.
16. X. Gao and X. Tang, "Automatic news video caption extraction and recognition," Intelligent Data Engineering and Automated Learning I IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents, 2000.
17. A. Wernicke and R. Lienhart, "On the segmentation of text in videos," Multimedia and Expo, 2000. ICME 2000. 2000 IEEE International Conference on, vol. 3, pp. 1511-1514,2000.